{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rlskoeser/shxco-missingdata-specreading/blob/main/missing-data/Sco_missing_borrowing_activity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiPL0tt0H4Xm"
      },
      "source": [
        "# Predict Missing Borrowing Activity Data from Shakespeare and Company Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This notebook explores and predicts missing data related to borrowing. It contains the code for figure 8 in our paper."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWxoPmTTH5SD"
      },
      "source": [
        "## Setup Libraries and Load S&Co Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from datetime import date, timedelta\n",
        "import matplotlib.pyplot as plt\n",
        "import altair as alt\n",
        "import sys\n",
        "sys.path.append('..')\n",
        "\n",
        "from utils.missing_data_processing import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "DNumTuDJFxaR"
      },
      "outputs": [],
      "source": [
        "events_df, members_df, _, _  = load_initial_data()\n",
        "events_df = preprocess_events_data(events_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyHN28FzIDKD"
      },
      "source": [
        "## Identify borrows and subscriptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The section filters event data to separate borrow and subscription events, and fills missing subscription volumes with a default value. It then associates borrow events with corresponding subscriptions based on member ID and event dates, and calculates the number of borrow events that occur during each subscription period. By associating borrow events with corresponding subscriptions, we can gain insights into user behavior during their subscription periods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "F3D8BMaMF1oL",
        "outputId": "0382f25d-1430-48b2-be83-2b5f060c0e26"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_type</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Claude Cahun / Mlle Lucie Schwob</td>\n",
              "      <td>Cahun, Claude</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 month</td>\n",
              "      <td>30.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Lending Library Card;Logbook</td>\n",
              "      <td>Sylvia Beach, Lucie Schwob Lending Library Car...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/3a...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>cahun</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29915</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Marcelle Flot</td>\n",
              "      <td>Flot, Marcelle</td>\n",
              "      <td>28.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 year</td>\n",
              "      <td>366.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Logbook</td>\n",
              "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>flot-marcelle</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 34 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         event_type  start_date    end_date  \\\n",
              "671    Subscription  1919-11-17  1919-12-17   \n",
              "29915  Subscription  1919-11-17  1920-11-17   \n",
              "\n",
              "                                             member_uris  \\\n",
              "671    https://shakespeareandco.princeton.edu/members...   \n",
              "29915  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "                           member_names member_sort_names  \\\n",
              "671    Claude Cahun / Mlle Lucie Schwob     Cahun, Claude   \n",
              "29915                     Marcelle Flot    Flot, Marcelle   \n",
              "\n",
              "       subscription_price_paid  subscription_deposit subscription_duration  \\\n",
              "671                        4.0                   NaN               1 month   \n",
              "29915                     28.0                   NaN                1 year   \n",
              "\n",
              "       subscription_duration_days  ...                   source_type  \\\n",
              "671                          30.0  ...  Lending Library Card;Logbook   \n",
              "29915                       366.0  ...                       Logbook   \n",
              "\n",
              "                                         source_citation  \\\n",
              "671    Sylvia Beach, Lucie Schwob Lending Library Car...   \n",
              "29915  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
              "\n",
              "                                         source_manifest  \\\n",
              "671    https://figgy.princeton.edu/concern/scanned_re...   \n",
              "29915                                                NaN   \n",
              "\n",
              "                                            source_image  \\\n",
              "671    https://iiif.princeton.edu/loris/figgy_prod/3a...   \n",
              "29915                                                NaN   \n",
              "\n",
              "                                        first_member_uri  second_member_uri  \\\n",
              "671    https://shakespeareandco.princeton.edu/members...               None   \n",
              "29915  https://shakespeareandco.princeton.edu/members...               None   \n",
              "\n",
              "           member_id item_id start_datetime end_datetime  \n",
              "671            cahun    None     1919-11-17   1919-12-17  \n",
              "29915  flot-marcelle    None     1919-11-17   1920-11-17  \n",
              "\n",
              "[2 rows x 34 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Filter the events dataframe to include only events with complete start and end dates.\n",
        "# Note: This step might exclude borrow events with no end date. Consider the impact of this exclusion on the analysis.\n",
        "date_events = events_df[(events_df.start_date.str.len() > 9) & (events_df.end_date.str.len() > 9)].copy()\n",
        "\n",
        "# Convert the 'start_date' and 'end_date' columns to datetime format. The 'errors' parameter is set to 'ignore' to skip any errors encountered during the conversion.\n",
        "date_events['start_datetime'] = pd.to_datetime(date_events.start_date, format='%Y-%m-%d', errors='ignore')\n",
        "date_events['end_datetime'] = pd.to_datetime(date_events.end_date, format='%Y-%m-%d', errors='ignore')\n",
        "\n",
        "# Sort the dataframe by 'start_datetime'.\n",
        "date_events = date_events.sort_values(by=['start_datetime'])\n",
        "\n",
        "# Display the first two rows of the dataframe for a quick check.\n",
        "date_events.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "R87karjRF4KW"
      },
      "outputs": [],
      "source": [
        "# Filter the 'date_events' DataFrame to include only borrow events.\n",
        "borrow_events = date_events[date_events.event_type == 'Borrow']\n",
        "\n",
        "# Filter the 'date_events' DataFrame to include only subscription events with complete dates.\n",
        "# The event types considered as subscription events are 'Subscription', 'Renewal', and 'Supplement'.\n",
        "subscription_events = date_events[date_events.event_type.isin(['Subscription', 'Renewal', 'Supplement'])].copy()\n",
        "\n",
        "# Some subscriptions (~377) do not have a documented subscription volume.\n",
        "# For these subscriptions, assume a minimum subscription volume of 1.\n",
        "subscription_events[\"subscription_volumes\"] = subscription_events.subscription_volumes.apply(lambda x: x if pd.notna(x) else 1)\n",
        "\n",
        "# Determine the date range for subscriptions.\n",
        "# The earliest date is the start date of the first event in 'date_events'.\n",
        "# The end date is the end date of the last subscription in 'subscription_events'.\n",
        "earliest_date = date_events.start_datetime.iloc[0]\n",
        "subs_end_date = subscription_events.end_datetime.max()\n",
        "\n",
        "# For now, use the 'has_card' field to identify borrows that belong to subscriptions.\n",
        "# Note: This is an approximation and may not be accurate.\n",
        "member_subs = subscription_events.copy()\n",
        "# Split the 'member_uris' field into 'first_member_uri' and 'second_member_uri' fields.\n",
        "# This is done to handle joint accounts in subscriptions.\n",
        "member_subs[['first_member_uri','second_member_uri']] = member_subs.member_uris.str.split(';', expand=True)\n",
        "# Merge 'member_subs' with 'members_df' on 'first_member_uri' to get the 'has_card' field.\n",
        "member_subs = pd.merge(left=member_subs, right=members_df, left_on=\"first_member_uri\", right_on=\"uri\")\n",
        "# Filter 'member_subs' to include only subscriptions where 'has_card' is True.\n",
        "member_subs = member_subs[member_subs.has_card]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAgQbL0zII5k"
      },
      "source": [
        "### Associating Borrow Events with Corresponding Subscriptions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "LJH_w9zUGAjX",
        "outputId": "92736799-f16a-4d29-c7ee-2fc23eaa2cd1"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>documented_borrows</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Claude Cahun / Mlle Lucie Schwob</td>\n",
              "      <td>Cahun, Claude</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 month</td>\n",
              "      <td>30.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Lucie Schwob Lending Library Car...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/3a...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>cahun</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29915</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Marcelle Flot</td>\n",
              "      <td>Flot, Marcelle</td>\n",
              "      <td>28.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 year</td>\n",
              "      <td>366.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>flot-marcelle</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         event_type  start_date    end_date  \\\n",
              "671    Subscription  1919-11-17  1919-12-17   \n",
              "29915  Subscription  1919-11-17  1920-11-17   \n",
              "\n",
              "                                             member_uris  \\\n",
              "671    https://shakespeareandco.princeton.edu/members...   \n",
              "29915  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "                           member_names member_sort_names  \\\n",
              "671    Claude Cahun / Mlle Lucie Schwob     Cahun, Claude   \n",
              "29915                     Marcelle Flot    Flot, Marcelle   \n",
              "\n",
              "       subscription_price_paid  subscription_deposit subscription_duration  \\\n",
              "671                        4.0                   NaN               1 month   \n",
              "29915                     28.0                   NaN                1 year   \n",
              "\n",
              "       subscription_duration_days  ...  \\\n",
              "671                          30.0  ...   \n",
              "29915                       366.0  ...   \n",
              "\n",
              "                                         source_citation  \\\n",
              "671    Sylvia Beach, Lucie Schwob Lending Library Car...   \n",
              "29915  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
              "\n",
              "                                         source_manifest  \\\n",
              "671    https://figgy.princeton.edu/concern/scanned_re...   \n",
              "29915                                                NaN   \n",
              "\n",
              "                                            source_image  \\\n",
              "671    https://iiif.princeton.edu/loris/figgy_prod/3a...   \n",
              "29915                                                NaN   \n",
              "\n",
              "                                        first_member_uri second_member_uri  \\\n",
              "671    https://shakespeareandco.princeton.edu/members...              None   \n",
              "29915  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "           member_id  item_id start_datetime end_datetime documented_borrows  \n",
              "671            cahun     None     1919-11-17   1919-12-17                  3  \n",
              "29915  flot-marcelle     None     1919-11-17   1920-11-17                  0  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a copy of the 'subscription_events' DataFrame to avoid SettingWithCopyWarning in pandas.\n",
        "subscriptions_df = subscription_events.copy()\n",
        "\n",
        "def count_borrows_during_subscription(subscription):\n",
        "    \"\"\"\n",
        "    Count the number of borrow events that start during the subscription period.\n",
        "\n",
        "    This function takes a subscription event and returns the number of borrow events that start during the subscription period.\n",
        "    Note: Only the start date of the borrow event is considered. If a borrow event starts during the subscription period and ends after the subscription period, it is still counted.\n",
        "\n",
        "    Args:\n",
        "        subscription (pd.Series): A subscription event.\n",
        "\n",
        "    Returns:\n",
        "        int: The number of borrow events that start during the subscription period.\n",
        "    \"\"\"\n",
        "    # Identify and count any borrow events that start during this subscription.\n",
        "    # This is done by filtering 'borrow_events' to include only events where the member ID matches the subscription's member ID,\n",
        "    # and the start date of the borrow event is within the subscription period.\n",
        "    return len(borrow_events[(borrow_events.member_id == subscription.member_id) & \n",
        "              (subscription.start_date <= borrow_events.start_date) & (borrow_events.start_date <= subscription.end_date)])\n",
        "\n",
        "# Apply the 'count_borrows_during_subscription' function to each subscription in 'subscriptions_df' to get the number of borrow events that start during each subscription period.\n",
        "# Store the results in a new column 'documented_borrows'.\n",
        "subscriptions_df['documented_borrows'] = subscriptions_df.apply(count_borrows_during_subscription, axis=1)\n",
        "\n",
        "# Display the first few rows of 'subscriptions_df' for a quick check.\n",
        "subscriptions_df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "_kJKlKYtGB0D",
        "outputId": "368e18cc-918c-4257-9241-243abe5f3aa9"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>documented_borrows</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29915</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Marcelle Flot</td>\n",
              "      <td>Flot, Marcelle</td>\n",
              "      <td>28.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 year</td>\n",
              "      <td>366.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>flot-marcelle</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29914</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-02-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Mrs. Worthing</td>\n",
              "      <td>Worthing, Mrs.</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3 months</td>\n",
              "      <td>92.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>worthing</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-02-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         event_type  start_date    end_date  \\\n",
              "29915  Subscription  1919-11-17  1920-11-17   \n",
              "29914  Subscription  1919-11-17  1920-02-17   \n",
              "\n",
              "                                             member_uris   member_names  \\\n",
              "29915  https://shakespeareandco.princeton.edu/members...  Marcelle Flot   \n",
              "29914  https://shakespeareandco.princeton.edu/members...  Mrs. Worthing   \n",
              "\n",
              "      member_sort_names  subscription_price_paid  subscription_deposit  \\\n",
              "29915    Flot, Marcelle                     28.0                   NaN   \n",
              "29914    Worthing, Mrs.                     12.0                   NaN   \n",
              "\n",
              "      subscription_duration  subscription_duration_days  ...  \\\n",
              "29915                1 year                       366.0  ...   \n",
              "29914              3 months                        92.0  ...   \n",
              "\n",
              "                                         source_citation source_manifest  \\\n",
              "29915  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...             NaN   \n",
              "29914  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...             NaN   \n",
              "\n",
              "      source_image                                   first_member_uri  \\\n",
              "29915          NaN  https://shakespeareandco.princeton.edu/members...   \n",
              "29914          NaN  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "      second_member_uri      member_id  item_id start_datetime end_datetime  \\\n",
              "29915              None  flot-marcelle     None     1919-11-17   1920-11-17   \n",
              "29914              None       worthing     None     1919-11-17   1920-02-17   \n",
              "\n",
              "      documented_borrows  \n",
              "29915                  0  \n",
              "29914                  0  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check — example subscriptions with no borrows\n",
        "subscriptions_df[subscriptions_df.documented_borrows == 0].head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "roaGbc3cGZxP",
        "outputId": "e4f8296e-774c-4708-9cdb-9b1e101df171"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>documented_borrows</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Claude Cahun / Mlle Lucie Schwob</td>\n",
              "      <td>Cahun, Claude</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 month</td>\n",
              "      <td>30.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Lucie Schwob Lending Library Car...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/3a...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>cahun</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-18</td>\n",
              "      <td>1919-12-18</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Henri Regnier</td>\n",
              "      <td>Regnier, Henri</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1 month</td>\n",
              "      <td>30.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
              "      <td>;https://figgy.princeton.edu/concern/scanned_r...</td>\n",
              "      <td>;https://iiif.princeton.edu/loris/figgy_prod/c...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>regnier</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-18</td>\n",
              "      <td>1919-12-18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       event_type  start_date    end_date  \\\n",
              "671  Subscription  1919-11-17  1919-12-17   \n",
              "672  Subscription  1919-11-18  1919-12-18   \n",
              "\n",
              "                                           member_uris  \\\n",
              "671  https://shakespeareandco.princeton.edu/members...   \n",
              "672  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "                         member_names member_sort_names  \\\n",
              "671  Claude Cahun / Mlle Lucie Schwob     Cahun, Claude   \n",
              "672                     Henri Regnier    Regnier, Henri   \n",
              "\n",
              "     subscription_price_paid  subscription_deposit subscription_duration  \\\n",
              "671                      4.0                   NaN               1 month   \n",
              "672                      4.0                   5.6               1 month   \n",
              "\n",
              "     subscription_duration_days  ...  \\\n",
              "671                        30.0  ...   \n",
              "672                        30.0  ...   \n",
              "\n",
              "                                       source_citation  \\\n",
              "671  Sylvia Beach, Lucie Schwob Lending Library Car...   \n",
              "672  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
              "\n",
              "                                       source_manifest  \\\n",
              "671  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "672  ;https://figgy.princeton.edu/concern/scanned_r...   \n",
              "\n",
              "                                          source_image  \\\n",
              "671  https://iiif.princeton.edu/loris/figgy_prod/3a...   \n",
              "672  ;https://iiif.princeton.edu/loris/figgy_prod/c...   \n",
              "\n",
              "                                      first_member_uri second_member_uri  \\\n",
              "671  https://shakespeareandco.princeton.edu/members...              None   \n",
              "672  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "     member_id  item_id start_datetime end_datetime documented_borrows  \n",
              "671      cahun     None     1919-11-17   1919-12-17                  3  \n",
              "672    regnier     None     1919-11-18   1919-12-18                  1  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check — example subscriptions with at least one borrow\n",
        "subscriptions_df[subscriptions_df.documented_borrows > 0].head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "nCX8z7_QGbfc"
      },
      "outputs": [],
      "source": [
        "# Identify the subset of subscriptions with at least one borrow event\n",
        "subs_with_borrows = subscriptions_df[subscriptions_df.documented_borrows > 0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69g5bXPxIN7E"
      },
      "source": [
        "### Mapping Borrow Events to Their Corresponding Subscription Periods"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "cUD94gUrGdWF"
      },
      "outputs": [],
      "source": [
        "# Create a copy of the 'borrow_events' DataFrame to avoid modifying the original DataFrame.\n",
        "borrow_events_df = borrow_events.copy()\n",
        "\n",
        "def is_borrow_within_subscription_period(borrow):\n",
        "    \"\"\"\n",
        "    Determine if a borrow event falls within a subscription period.\n",
        "\n",
        "    This function takes a borrow event and checks if it falls within a subscription period for the same member. It returns True if the borrow event falls within a subscription period, and False otherwise.\n",
        "\n",
        "    Args:\n",
        "        borrow (pd.Series): A borrow event.\n",
        "\n",
        "    Returns:\n",
        "        bool: True if the borrow event falls within a subscription period, False otherwise.\n",
        "    \"\"\"\n",
        "    # Check if the borrow event falls within a subscription period for the same member.\n",
        "    # This is done by filtering 'subscriptions_df' to include only subscriptions where the member ID matches the borrow event's member ID,\n",
        "    # and the start date of the borrow event is within the subscription period.\n",
        "    return bool(len(subscriptions_df[(subscriptions_df.member_id == borrow.member_id) & \n",
        "              (subscriptions_df.start_date <= borrow.start_date) &\n",
        "              (subscriptions_df.end_date >= borrow.start_date)]))\n",
        "\n",
        "# Apply the 'is_borrow_within_subscription_period' function to each borrow event in 'borrow_events_df' to determine if it falls within a subscription period.\n",
        "# Store the results in a new column 'within_subscription'.\n",
        "borrow_events_df['within_subscription'] = borrow_events_df.apply(is_borrow_within_subscription_period, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "2L_sB-K5Ghof",
        "outputId": "8c6c7df0-19dc-4829-ff98-566920f6504b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>within_subscription</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>768</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1920-01-12</td>\n",
              "      <td>1920-01-22</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Blanche Reverchon</td>\n",
              "      <td>Reverchon, Blanche</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Blanche Reverchon Lending Librar...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/7e...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>reverchon</td>\n",
              "      <td>galsworthy-addresses-america-1919</td>\n",
              "      <td>1920-01-12</td>\n",
              "      <td>1920-01-22</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>779</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1920-01-22</td>\n",
              "      <td>1920-03-13</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Blanche Reverchon</td>\n",
              "      <td>Reverchon, Blanche</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Blanche Reverchon Lending Librar...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/7e...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>reverchon</td>\n",
              "      <td>dreiser-sister-carrie</td>\n",
              "      <td>1920-01-22</td>\n",
              "      <td>1920-03-13</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    event_type  start_date    end_date  \\\n",
              "768     Borrow  1920-01-12  1920-01-22   \n",
              "779     Borrow  1920-01-22  1920-03-13   \n",
              "\n",
              "                                           member_uris       member_names  \\\n",
              "768  https://shakespeareandco.princeton.edu/members...  Blanche Reverchon   \n",
              "779  https://shakespeareandco.princeton.edu/members...  Blanche Reverchon   \n",
              "\n",
              "      member_sort_names  subscription_price_paid  subscription_deposit  \\\n",
              "768  Reverchon, Blanche                      NaN                   NaN   \n",
              "779  Reverchon, Blanche                      NaN                   NaN   \n",
              "\n",
              "    subscription_duration  subscription_duration_days  ...  \\\n",
              "768                   NaN                         NaN  ...   \n",
              "779                   NaN                         NaN  ...   \n",
              "\n",
              "                                       source_citation  \\\n",
              "768  Sylvia Beach, Blanche Reverchon Lending Librar...   \n",
              "779  Sylvia Beach, Blanche Reverchon Lending Librar...   \n",
              "\n",
              "                                       source_manifest  \\\n",
              "768  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "779  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "\n",
              "                                          source_image  \\\n",
              "768  https://iiif.princeton.edu/loris/figgy_prod/7e...   \n",
              "779  https://iiif.princeton.edu/loris/figgy_prod/7e...   \n",
              "\n",
              "                                      first_member_uri second_member_uri  \\\n",
              "768  https://shakespeareandco.princeton.edu/members...              None   \n",
              "779  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "     member_id                            item_id start_datetime end_datetime  \\\n",
              "768  reverchon  galsworthy-addresses-america-1919     1920-01-12   1920-01-22   \n",
              "779  reverchon              dreiser-sister-carrie     1920-01-22   1920-03-13   \n",
              "\n",
              "    within_subscription  \n",
              "768               False  \n",
              "779               False  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check to confirm\n",
        "borrow_events_df[~borrow_events_df.within_subscription].head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "xNn9wp_JGj23"
      },
      "outputs": [],
      "source": [
        "borrows_within_subscriptions = borrow_events_df[borrow_events_df.within_subscription]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rPU3lAyZISbr"
      },
      "source": [
        "## Borrowed books and subscription volumes by day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "QwuKIDHoGpC6",
        "outputId": "7c014a1f-e9b6-4ed3-af03-4496e616b36b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>books_out</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-18</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  books_out  borrow_event_count  subscription_volumes  \\\n",
              "0 1919-11-17          0                   0                   3.0   \n",
              "1 1919-11-18          3                   3                   9.0   \n",
              "\n",
              "   card_subscription_volumes  book_subscription_volumes  \n",
              "0                        1.0                        1.0  \n",
              "1                        4.0                        4.0  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Initialize a one-day timedelta for incrementing the date.\n",
        "one_day = timedelta(days=1)\n",
        "\n",
        "# Initialize the start date as the earliest start date in 'date_events'.\n",
        "day = date_events.start_datetime.iloc[0]\n",
        "\n",
        "# Initialize lists to store the data for each date.\n",
        "dates = []\n",
        "books_out = []\n",
        "borrow_events_perday = []\n",
        "subscription_vols = []\n",
        "card_subscription_vols = []\n",
        "book_subscription_vols = []\n",
        "\n",
        "# Iterate over each date until the end of the last subscription.\n",
        "while day < subs_end_date:\n",
        "    # Append the current date to 'dates'.\n",
        "    dates.append(day)\n",
        "\n",
        "    # Count the number of borrow events that span the current date and append the count to 'books_out'.\n",
        "    books_out.append(len(borrows_within_subscriptions[(borrows_within_subscriptions.start_datetime <= day) & (borrows_within_subscriptions.end_datetime > day)]))\n",
        "\n",
        "    # Sum the volumes of active subscriptions on the current date and append the sum to 'subscription_vols'.\n",
        "    subscription_vols.append(subscription_events[(subscription_events.start_datetime <= day) & (day < subscription_events.end_datetime)].subscription_volumes.sum())\n",
        "\n",
        "    # Sum the volumes of active subscriptions for members with extant cards on the current date and append the sum to 'card_subscription_vols'.\n",
        "    card_subscription_vols.append(member_subs[(member_subs.start_datetime <= day) & (day < member_subs.end_datetime)].subscription_volumes.sum())\n",
        "\n",
        "    # Sum the volumes of active subscriptions with at least one borrow event on the current date and append the sum to 'book_subscription_vols'.\n",
        "    book_subscription_vols.append(subs_with_borrows[(subs_with_borrows.start_datetime <= day) & (day < subs_with_borrows.end_datetime)].subscription_volumes.sum())\n",
        "\n",
        "    # Count the borrow events that started on the current date and append the count to 'borrow_events_perday'.\n",
        "    borrow_events_perday.append(len(borrow_events[borrow_events.start_datetime == day]))\n",
        "\n",
        "    # Increment the date by one day.\n",
        "    day += one_day\n",
        "\n",
        "# Create a DataFrame 'borrowing_df' to store the data for each date.\n",
        "borrowing_df  = pd.DataFrame({\n",
        "    'date': dates,\n",
        "    'books_out': books_out,\n",
        "    'borrow_event_count': borrow_events_perday,\n",
        "    'subscription_volumes': subscription_vols,\n",
        "    'card_subscription_volumes': card_subscription_vols,\n",
        "    'book_subscription_volumes': book_subscription_vols\n",
        "})\n",
        "\n",
        "borrowing_df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "O142DeedGrFt",
        "outputId": "a28eff94-95fa-46f3-8898-d0dbcbd7e28e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>books_out</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1919-11-22</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1919-11-23</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  books_out  borrow_event_count  subscription_volumes  \\\n",
              "5 1919-11-22          4                   2                  14.0   \n",
              "6 1919-11-23          4                   0                  14.0   \n",
              "\n",
              "   card_subscription_volumes  book_subscription_volumes  \n",
              "5                        6.0                        5.0  \n",
              "6                        6.0                        5.0  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# where do card member subs and book subs differ?\n",
        "borrowing_df[borrowing_df.card_subscription_volumes != borrowing_df.book_subscription_volumes].head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "SkT0Al8LGsfs",
        "outputId": "4c2ad534-c1a6-4c84-d62b-2210fd048fa8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>books_out</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>book_events_per_subscription_vol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-18</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  books_out  borrow_event_count  subscription_volumes  \\\n",
              "0 1919-11-17          0                   0                   3.0   \n",
              "1 1919-11-18          3                   3                   9.0   \n",
              "\n",
              "   card_subscription_volumes  book_subscription_volumes  \\\n",
              "0                        1.0                        1.0   \n",
              "1                        4.0                        4.0   \n",
              "\n",
              "   book_events_per_subscription_vol  \n",
              "0                              0.00  \n",
              "1                              0.75  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# what is the ratio of subscriptions to borrow events?\n",
        "borrowing_df['book_events_per_subscription_vol'] = borrowing_df.apply(lambda x: x.borrow_event_count / x.book_subscription_volumes, axis=1)\n",
        "borrowing_df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IQ_awXNGuQw",
        "outputId": "f93e33ec-d68a-4d9e-e197-2188e4b6d990"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    8326.000000\n",
              "mean        0.094945\n",
              "std         0.142912\n",
              "min         0.000000\n",
              "25%         0.000000\n",
              "50%         0.069767\n",
              "75%         0.146341\n",
              "max         6.000000\n",
              "Name: book_events_per_subscription_vol, dtype: float64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "borrowing_df['book_events_per_subscription_vol'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "L_qOJa4gGvsJ",
        "outputId": "bb9a77b2-ce32-4ada-834d-9a744b3c9b14"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-18</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1919-11-19</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1919-11-20</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1919-11-21</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1919-11-22</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1919-11-23</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1919-11-24</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1919-11-25</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1919-11-26</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1919-11-27</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1919-11-28</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1919-11-29</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  book_subscription_volumes  borrow_event_count  \\\n",
              "0  1919-11-17                        1.0                   0   \n",
              "1  1919-11-18                        4.0                   3   \n",
              "2  1919-11-19                        5.0                   2   \n",
              "3  1919-11-20                        5.0                   0   \n",
              "4  1919-11-21                        5.0                   0   \n",
              "5  1919-11-22                        5.0                   2   \n",
              "6  1919-11-23                        5.0                   0   \n",
              "7  1919-11-24                        5.0                   1   \n",
              "8  1919-11-25                        6.0                   1   \n",
              "9  1919-11-26                        6.0                   0   \n",
              "10 1919-11-27                        6.0                   0   \n",
              "11 1919-11-28                        6.0                   2   \n",
              "12 1919-11-29                        7.0                   1   \n",
              "\n",
              "    card_subscription_volumes  subscription_volumes  \n",
              "0                         1.0                   3.0  \n",
              "1                         4.0                   9.0  \n",
              "2                         5.0                  10.0  \n",
              "3                         5.0                  11.0  \n",
              "4                         5.0                  13.0  \n",
              "5                         6.0                  14.0  \n",
              "6                         6.0                  14.0  \n",
              "7                         7.0                  16.0  \n",
              "8                         8.0                  17.0  \n",
              "9                         8.0                  19.0  \n",
              "10                        8.0                  19.0  \n",
              "11                        8.0                  20.0  \n",
              "12                        9.0                  27.0  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# aggregate subscriptions by day so that we can get a weekly/monthly average of the _total_ subscription volumes\n",
        "\n",
        "borrowing_daily = borrowing_df.groupby([pd.Grouper(key='date', freq='D')]) \\\n",
        "  .agg({'book_subscription_volumes':'sum',\n",
        "        'borrow_event_count': 'sum', 'card_subscription_volumes':'sum', \n",
        "        'book_subscription_volumes': 'sum', \n",
        "        'subscription_volumes': 'sum'}).reset_index()\n",
        "borrowing_daily.head(13)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77fsWXMIIaK5"
      },
      "source": [
        "### Aggregate borrowed books and subscriptions by week"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ict5aji7Gx23",
        "outputId": "372106a6-6ba0-4a57-80bf-dccf51657abe"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-23</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-30</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  book_subscription_volumes  borrow_event_count  \\\n",
              "0 1919-11-23                        5.0                   7   \n",
              "1 1919-11-30                        7.0                   5   \n",
              "\n",
              "   card_subscription_volumes  subscription_volumes  \n",
              "0                        6.0                  14.0  \n",
              "1                        9.0                  27.0  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Aggregate the daily borrowing data into weekly data.\n",
        "# For each week, we calculate:\n",
        "# - the maximum number of book subscription volumes ('book_subscription_volumes')\n",
        "# - the total number of borrow events ('borrow_event_count')\n",
        "# - the maximum number of card subscription volumes ('card_subscription_volumes')\n",
        "# - the maximum number of subscription volumes ('subscription_volumes')\n",
        "# The 'pd.Grouper' function is used to group the data by week.\n",
        "borrowing_weekly = borrowing_daily.groupby([pd.Grouper(key='date', freq='W')]).agg({\n",
        "    'book_subscription_volumes':'max',\n",
        "    'borrow_event_count': 'sum', \n",
        "    'card_subscription_volumes':'max', \n",
        "    'subscription_volumes': 'max'\n",
        "})\n",
        "\n",
        "# Reset the index of the DataFrame to make 'date' a column again.\n",
        "borrowing_weekly = borrowing_weekly.reset_index()\n",
        "\n",
        "# Display the 'borrowing_weekly' DataFrame.\n",
        "borrowing_weekly.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8dVYUllG3yi",
        "outputId": "558a1148-f5c1-4eef-efec-a65f2caf2df7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    1190.000000\n",
              "mean       16.490756\n",
              "std        12.728959\n",
              "min         0.000000\n",
              "25%         8.000000\n",
              "50%        13.000000\n",
              "75%        23.000000\n",
              "max        84.000000\n",
              "Name: borrow_event_count, dtype: float64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "borrowing_weekly.borrow_event_count.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1M_ACZRG5Uz",
        "outputId": "3d239ec2-b60b-43f2-e508-71fbd53882e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    1190.000000\n",
              "mean        0.642890\n",
              "std         0.364388\n",
              "min         0.000000\n",
              "25%         0.428571\n",
              "50%         0.636364\n",
              "75%         0.833333\n",
              "max         6.000000\n",
              "Name: ratio, dtype: float64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "borrowing_weekly['ratio'] = borrowing_weekly.apply(lambda row: row.borrow_event_count / row.book_subscription_volumes, axis=1)\n",
        "borrowing_weekly.ratio.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxlVRfxXIdFs"
      },
      "source": [
        "### Visualizing Borrowing Activity Over Time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "MQVU5aMLG69u",
        "outputId": "f01629bd-b05e-4501-bf32-d87007033500"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Get the current axis so we can plot multiple series on the same axis.\n",
        "ax = plt.gca()\n",
        "\n",
        "# Plot the weekly book subscription volumes over time.\n",
        "# The 'kind' parameter is set to 'line' to create a line plot.\n",
        "# The 'x' and 'y' parameters specify the data to plot on the x-axis and y-axis.\n",
        "# The 'ax' parameter is set to the current axis to plot on the same axis.\n",
        "# The 'figsize' parameter sets the size of the figure.\n",
        "# The 'title' parameter sets the title of the plot.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='book_subscription_volumes', ax=ax, figsize=(25,7), title='book events + total subscription volumes (by week)')\n",
        "\n",
        "# Plot the weekly count of borrow events over time on the same axis.\n",
        "# The 'color' parameter is set to 'green' to differentiate this line from the others.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='borrow_event_count', color='green', ax=ax)\n",
        "\n",
        "# Plot the ratio of borrow events to book subscription volumes over time on the same axis.\n",
        "# The 'color' parameter is set to 'orange' to differentiate this line from the others.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='ratio', color='orange', ax=ax)\n",
        "\n",
        "# Display the plot.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "CsYBZJnZG8vO",
        "outputId": "8b08d1bd-1b77-46f3-ae2e-69e801e9c07c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Compare book subscription volumes with card subscription volumes over time to see how different they are.\n",
        "\n",
        "# Get the current axis so we can plot multiple series on the same axis.\n",
        "ax = plt.gca()\n",
        "\n",
        "# Plot the weekly book subscription volumes over time.\n",
        "# The 'kind' parameter is set to 'line' to create a line plot.\n",
        "# The 'x' and 'y' parameters specify the data to plot on the x-axis and y-axis.\n",
        "# The 'ax' parameter is set to the current axis to plot on the same axis.\n",
        "# The 'figsize' parameter sets the size of the figure.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='book_subscription_volumes', ax=ax, figsize=(25,7))\n",
        "\n",
        "# Plot the weekly card subscription volumes over time on the same axis.\n",
        "# The 'color' parameter is set to 'green' to differentiate this line from the book subscription volumes.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='card_subscription_volumes', color='green', ax=ax)\n",
        "\n",
        "# Display the plot.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1aaXIQ3XHO3-",
        "outputId": "4d733c0a-1a80-46cf-aa77-052f3f6c7d44"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-01</td>\n",
              "      <td>7.0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-12-01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  book_subscription_volumes  borrow_event_count\n",
              "0 1919-11-01                        7.0                  12\n",
              "1 1919-12-01                       10.0                  30"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Aggregate the daily borrowing data into monthly data.\n",
        "# For each month, we calculate:\n",
        "# - the maximum number of book subscription volumes ('book_subscription_volumes')\n",
        "# - the total number of borrow events ('borrow_event_count')\n",
        "# The 'pd.Grouper' function is used to group the data by month.\n",
        "borrowing_monthly = borrowing_daily.groupby([pd.Grouper(key='date', freq='MS')]).agg({\n",
        "    'book_subscription_volumes':'max',\n",
        "    'borrow_event_count': 'sum'\n",
        "})\n",
        "\n",
        "# Reset the index of the DataFrame to make 'date' a column again.\n",
        "borrowing_monthly = borrowing_monthly.reset_index()\n",
        "\n",
        "# Display the first two rows of 'borrowing_monthly' for a quick check.\n",
        "borrowing_monthly.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBpwSNGqHPE_",
        "outputId": "b0d14c0c-a0e9-4f22-b05e-56272d504f9e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    275.000000\n",
              "mean      71.360000\n",
              "std       50.290302\n",
              "min        0.000000\n",
              "25%       38.500000\n",
              "50%       53.000000\n",
              "75%       99.500000\n",
              "max      251.000000\n",
              "Name: borrow_event_count, dtype: float64"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "borrowing_monthly.borrow_event_count.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTWsNmyAHQna",
        "outputId": "bb27ac69-9f47-4222-cd23-522b53df482a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    275.000000\n",
              "mean       2.600836\n",
              "std        0.985389\n",
              "min        0.000000\n",
              "25%        1.935417\n",
              "50%        2.625000\n",
              "75%        3.264231\n",
              "max        6.000000\n",
              "Name: ratio, dtype: float64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "borrowing_monthly['ratio'] = borrowing_monthly.apply(lambda row: row.borrow_event_count / row.book_subscription_volumes, axis=1)\n",
        "borrowing_monthly.ratio.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "wTVATqw6HR5u",
        "outputId": "81948558-9acf-4895-e5da-61ea3cae1e32"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Get the current axis so we can plot multiple series on the same axis.\n",
        "ax = plt.gca()\n",
        "\n",
        "# Plot the monthly book subscription volumes over time.\n",
        "borrowing_monthly.plot(kind='line', x='date', y='book_subscription_volumes', ax=ax, figsize=(25,7), title='book events + average book subscription volumes (by month)')\n",
        "\n",
        "# Plot the monthly count of borrow events over time on the same axis.\n",
        "# The 'color' parameter is set to 'green' to differentiate this line from the others.\n",
        "borrowing_monthly.plot(kind='line', x='date', y='borrow_event_count', color='green', ax=ax)\n",
        "\n",
        "# Plot the ratio of borrow events to book subscription volumes over time on the same axis.\n",
        "# The 'color' parameter is set to 'orange' to differentiate this line from the others.\n",
        "borrowing_monthly.plot(kind='line', x='date', y='ratio', color='orange', ax=ax)\n",
        "\n",
        "# Display the plot.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "V4gSbgU-HToF",
        "outputId": "41fe0b24-1970-4082-f39d-1b484ac2a702"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# if we plot just the ratio, how stable is it?\n",
        "ax = plt.gca()\n",
        "\n",
        "borrowing_weekly.plot(kind='line',x='date',y='ratio', color='blue', ax=ax)\n",
        "borrowing_monthly.plot(kind='line',x='date',y='ratio', color='green', ax=ax)\n",
        "\n",
        "# monthly ratio average\n",
        "#plt.axhline(y=2.858, color='yellow', linestyle='-')  # 2.8 is ratio when we use average subscription\n",
        "plt.axhline(y=2.6, color='yellow', linestyle='-')  # 2.6 is ratio when we use max # subscriptions\n",
        "\n",
        "\n",
        "# weekly ratio average\n",
        "plt.axhline(y=0.663, color='purple', linestyle='-')  # weekly ratio doesn't change noticeably when we switch from mean to max\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gr7Syd1LHVU3",
        "outputId": "9ee5a90e-a6a0-4445-b8dc-b7e55de2e5a3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>ratio</th>\n",
              "      <th>non_book_subs_vols</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-23</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-30</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  book_subscription_volumes  borrow_event_count  \\\n",
              "0 1919-11-23                        5.0                   7   \n",
              "1 1919-11-30                        7.0                   5   \n",
              "\n",
              "   card_subscription_volumes  subscription_volumes     ratio  \\\n",
              "0                        6.0                  14.0  1.400000   \n",
              "1                        9.0                  27.0  0.714286   \n",
              "\n",
              "   non_book_subs_vols  \n",
              "0                 9.0  \n",
              "1                20.0  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ok, now apply our ratio to the non-book subscription volumes\n",
        "borrowing_weekly['non_book_subs_vols'] = borrowing_weekly.apply(lambda row: row.subscription_volumes - row.book_subscription_volumes, axis=1)\n",
        "borrowing_weekly.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvpafwR2HWsx",
        "outputId": "1870ed6a-ed00-4dcd-8eb6-e9132243015a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average borrow events per book subscription volume: 0.6428904216675599\n"
          ]
        }
      ],
      "source": [
        "borrow_events_per_subsvol = borrowing_weekly.ratio.mean()\n",
        "print(f'Average borrow events per book subscription volume: {borrow_events_per_subsvol}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "cWtSO1lXHX8P",
        "outputId": "de5cd33d-995b-48eb-c3e3-842b8e6752d4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>ratio</th>\n",
              "      <th>non_book_subs_vols</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1920-03-07</td>\n",
              "      <td>11.0</td>\n",
              "      <td>7</td>\n",
              "      <td>15.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1920-10-17</td>\n",
              "      <td>13.0</td>\n",
              "      <td>8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>67.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>1920-12-05</td>\n",
              "      <td>19.0</td>\n",
              "      <td>12</td>\n",
              "      <td>26.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>107.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>1921-04-17</td>\n",
              "      <td>13.0</td>\n",
              "      <td>8</td>\n",
              "      <td>22.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>134.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>1921-06-26</td>\n",
              "      <td>13.0</td>\n",
              "      <td>8</td>\n",
              "      <td>20.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  book_subscription_volumes  borrow_event_count  \\\n",
              "15 1920-03-07                       11.0                   7   \n",
              "47 1920-10-17                       13.0                   8   \n",
              "54 1920-12-05                       19.0                  12   \n",
              "73 1921-04-17                       13.0                   8   \n",
              "83 1921-06-26                       13.0                   8   \n",
              "\n",
              "    card_subscription_volumes  subscription_volumes     ratio  \\\n",
              "15                       15.0                  82.0  0.636364   \n",
              "47                       19.0                  80.0  0.615385   \n",
              "54                       26.0                 126.0  0.631579   \n",
              "73                       22.0                 147.0  0.615385   \n",
              "83                       20.0                 133.0  0.615385   \n",
              "\n",
              "    non_book_subs_vols  \n",
              "15                71.0  \n",
              "47                67.0  \n",
              "54               107.0  \n",
              "73               134.0  \n",
              "83               120.0  "
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Filter the 'borrowing_weekly' DataFrame to include only rows where the 'ratio' is between 0.6 and 0.65.\n",
        "# The '&' operator is used to combine the conditions.\n",
        "# The result is a subset of 'borrowing_weekly' where the 'ratio' is within the specified range.\n",
        "# This could be useful for identifying weeks where the ratio of borrow events to book subscription volumes is within a certain range.\n",
        "filtered_borrowing_weekly = borrowing_weekly[(borrowing_weekly.ratio >= 0.6) & (borrowing_weekly.ratio <= 0.65)]\n",
        "filtered_borrowing_weekly.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "snUj1N2gHZHe",
        "outputId": "4ba3e23e-59c8-4141-9ce5-af8bc3813075"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>ratio</th>\n",
              "      <th>non_book_subs_vols</th>\n",
              "      <th>estimated_borrow_events</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-23</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.786014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-30</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>20.0</td>\n",
              "      <td>12.857808</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  book_subscription_volumes  borrow_event_count  \\\n",
              "0 1919-11-23                        5.0                   7   \n",
              "1 1919-11-30                        7.0                   5   \n",
              "\n",
              "   card_subscription_volumes  subscription_volumes     ratio  \\\n",
              "0                        6.0                  14.0  1.400000   \n",
              "1                        9.0                  27.0  0.714286   \n",
              "\n",
              "   non_book_subs_vols  estimated_borrow_events  \n",
              "0                 9.0                 5.786014  \n",
              "1                20.0                12.857808  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Calculate the estimated number of borrow events for each week.\n",
        "# This is done by multiplying the number of non-book subscription volumes ('non_book_subs_vols') by the average number of borrow events per subscription volume ('borrow_events_per_subsvol').\n",
        "# The result is stored in a new column 'estimated_borrow_events'.\n",
        "borrowing_weekly['estimated_borrow_events'] = borrowing_weekly.non_book_subs_vols.apply(lambda x: x * borrow_events_per_subsvol)\n",
        "\n",
        "# Display the first two rows of 'borrowing_weekly' for a quick check.\n",
        "borrowing_weekly.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "6SxsiyAQHaYs",
        "outputId": "bde49492-3a66-43b6-a8c4-a9926fad13ad"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Get the current axis so we can plot multiple series on the same axis.\n",
        "ax = plt.gca()\n",
        "\n",
        "# Plot the weekly non-book subscription volumes over time.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='non_book_subs_vols', ax=ax, figsize=(25,7))\n",
        "\n",
        "# Plot the estimated weekly borrow events over time on the same axis.\n",
        "# The 'color' parameter is set to 'green' to differentiate this line from the non-book subscription volumes.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='estimated_borrow_events', color='green', ax=ax)\n",
        "\n",
        "# Display the plot.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "H7jA3gtoHcDB",
        "outputId": "4edd9f06-ec17-4cab-9bd3-e094f2a9dcbf"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Get the current axis so we can plot multiple series on the same axis.\n",
        "ax = plt.gca()\n",
        "\n",
        "# Calculate the total estimated borrow events for each week.\n",
        "# This is done by adding the estimated borrow events ('estimated_borrow_events') to the actual borrow event count ('borrow_event_count').\n",
        "# The result is stored in a new column 'total_estimated_borrow_events'.\n",
        "borrowing_weekly['total_estimated_borrow_events'] = borrowing_weekly.apply(lambda row: row.estimated_borrow_events + row.borrow_event_count, axis=1)\n",
        "\n",
        "# Plot the weekly subscription volumes over time.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='subscription_volumes', ax=ax, figsize=(25,7))\n",
        "\n",
        "# Plot the total estimated weekly borrow events over time on the same axis.\n",
        "# The 'color' parameter is set to 'green' to differentiate this line from the subscription volumes.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='total_estimated_borrow_events', color='green', ax=ax)\n",
        "\n",
        "# Plot the estimated weekly borrow events over time on the same axis.\n",
        "# The 'color' parameter is set to 'lightgreen' to differentiate this line from the others.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='estimated_borrow_events', color='lightgreen', ax=ax)\n",
        "\n",
        "# Plot the actual weekly borrow event count over time on the same axis.\n",
        "# The 'color' parameter is set to 'lightblue' to differentiate this line from the others.\n",
        "borrowing_weekly.plot(kind='line', x='date', y='borrow_event_count', color='lightblue', ax=ax)\n",
        "\n",
        "# Display the plot.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "58c71CNw7WUa",
        "outputId": "63338907-93ce-415a-9abb-22dd165e77ed"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>total</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-23</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Subscription volumes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-30</td>\n",
              "      <td>27.0</td>\n",
              "      <td>Subscription volumes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  total                  type\n",
              "0 1919-11-23   14.0  Subscription volumes\n",
              "1 1919-11-30   27.0  Subscription volumes"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Prepare the 'borrowing_weekly' DataFrame for plotting with Altair by flattening it and adding a 'type' column to differentiate the series.\n",
        "\n",
        "# Create a DataFrame 'alt_subs_vols' with the date and total subscription volumes for each week.\n",
        "# The 'rename' function is used to rename 'subscription_volumes' to 'total'.\n",
        "# The 'type' column is added to indicate that these are subscription volumes.\n",
        "alt_subs_vols = borrowing_weekly.rename(columns={\"subscription_volumes\": \"total\"})[[\"date\", \"total\"]]\n",
        "alt_subs_vols[\"type\"] = \"Subscription volumes\"\n",
        "\n",
        "# Create a DataFrame 'alt_borrow_count' with the date and borrow event count for each week.\n",
        "# The 'rename' function is used to rename 'borrow_event_count' to 'total'.\n",
        "# The 'type' column is added to indicate that these are borrow event counts.\n",
        "alt_borrow_count = borrowing_weekly.rename(columns={\"borrow_event_count\": \"total\"})[[\"date\", \"total\"]]\n",
        "alt_borrow_count[\"type\"] = \"Borrow event count\"\n",
        "\n",
        "# Create a DataFrame 'alt_est_borrow_count' with the date and estimated borrow events for each week.\n",
        "# The 'rename' function is used to rename 'estimated_borrow_events' to 'total'.\n",
        "# The 'type' column is added to indicate that these are estimated borrow events.\n",
        "alt_est_borrow_count = borrowing_weekly.rename(columns={\"estimated_borrow_events\": \"total\"})[[\"date\", \"total\"]]\n",
        "alt_est_borrow_count[\"type\"] = \"Estimated borrow events\"\n",
        "\n",
        "# Create a DataFrame 'alt_totalest_borrow_count' with the date and total estimated borrow events for each week.\n",
        "# The 'rename' function is used to rename 'total_estimated_borrow_events' to 'total'.\n",
        "# The 'type' column is added to indicate that these are total estimated borrow events.\n",
        "alt_totalest_borrow_count = borrowing_weekly.rename(columns={\"total_estimated_borrow_events\": \"total\"})[[\"date\", \"total\"]]\n",
        "alt_totalest_borrow_count[\"type\"] = \"Total estimated borrow events\"\n",
        "\n",
        "# Concatenate the four DataFrames into a single DataFrame 'alt_borrowing_weekly'.\n",
        "# This DataFrame can be used to create a plot with Altair where each series is color-coded based on the 'type' column.\n",
        "alt_borrowing_weekly = pd.concat([alt_subs_vols, alt_borrow_count, alt_est_borrow_count, alt_totalest_borrow_count])\n",
        "\n",
        "# Display the first two rows of 'alt_borrowing_weekly' for a quick check.\n",
        "alt_borrowing_weekly.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Figure - Borrowing activity, subscription volumes, and estimated borrowing activity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "OPPJ8TzwD80i",
        "outputId": "8c4abb43-c4f5-41df-db34-559749506202"
      },
      "outputs": [
        {
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4.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-08-23T00:00:00\", \"total\": 16.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-08-30T00:00:00\", \"total\": 6.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-09-06T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-09-13T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-09-20T00:00:00\", \"total\": 11.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-09-27T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-10-04T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-10-11T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-10-18T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-10-25T00:00:00\", \"total\": 16.0, \"type\": \"Borrow event count\"}, {\"date\": \"1931-11-01T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event 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\"1932-09-18T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-09-25T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-10-02T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-10-09T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-10-16T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-10-23T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-10-30T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-11-06T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-11-13T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-11-20T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-11-27T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-12-04T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-12-11T00:00:00\", \"total\": 16.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-12-18T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1932-12-25T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-01-01T00:00:00\", \"total\": 15.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-01-08T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-01-15T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-01-22T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-01-29T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-02-05T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-02-12T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-02-19T00:00:00\", \"total\": 11.0, \"type\": \"Borrow event 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\"1933-05-14T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-05-21T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-05-28T00:00:00\", \"total\": 11.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-06-04T00:00:00\", \"total\": 15.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-06-11T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-06-18T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-06-25T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-07-02T00:00:00\", \"total\": 4.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-07-09T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-07-16T00:00:00\", \"total\": 4.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-07-23T00:00:00\", \"total\": 5.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-07-30T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-08-06T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-08-13T00:00:00\", \"total\": 6.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-08-20T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-08-27T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-09-03T00:00:00\", \"total\": 5.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-09-10T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-09-17T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-09-24T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-10-01T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-10-08T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-10-15T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-10-22T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-10-29T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-11-05T00:00:00\", \"total\": 31.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-11-12T00:00:00\", \"total\": 31.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-11-19T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-11-26T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-12-03T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-12-10T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-12-17T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-12-24T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1933-12-31T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-01-07T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-01-14T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-01-21T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-01-28T00:00:00\", \"total\": 40.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-02-04T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-02-11T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-02-18T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-02-25T00:00:00\", \"total\": 29.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-03-04T00:00:00\", \"total\": 35.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-03-11T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-03-18T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-03-25T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-04-01T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-04-08T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-04-15T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-04-22T00:00:00\", \"total\": 34.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-04-29T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-05-06T00:00:00\", \"total\": 26.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-05-13T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-05-20T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-05-27T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-06-03T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-06-10T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-06-17T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-06-24T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-07-01T00:00:00\", \"total\": 11.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-07-08T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-07-15T00:00:00\", \"total\": 35.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-07-22T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-07-29T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-08-05T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-08-12T00:00:00\", \"total\": 15.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-08-19T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-08-26T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-09-02T00:00:00\", \"total\": 15.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-09-09T00:00:00\", \"total\": 15.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-09-16T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-09-23T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-09-30T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-10-07T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-10-14T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-10-21T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-10-28T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-11-04T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-11-11T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-11-18T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-11-25T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-12-02T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-12-09T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-12-16T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-12-23T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1934-12-30T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-01-06T00:00:00\", \"total\": 16.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-01-13T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-01-20T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-01-27T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-02-03T00:00:00\", 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\"Borrow event count\"}, {\"date\": \"1935-04-28T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-05-05T00:00:00\", \"total\": 26.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-05-12T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-05-19T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-05-26T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-06-02T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-06-09T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-06-16T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-06-23T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-06-30T00:00:00\", \"total\": 15.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-07-07T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-07-14T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-07-21T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-07-28T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-08-04T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-08-11T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-08-18T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-08-25T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-09-01T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-09-08T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-09-15T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-09-22T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-09-29T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-10-06T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-10-13T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-10-20T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-10-27T00:00:00\", \"total\": 35.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-11-03T00:00:00\", \"total\": 31.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-11-10T00:00:00\", \"total\": 37.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-11-17T00:00:00\", \"total\": 34.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-11-24T00:00:00\", \"total\": 40.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-12-01T00:00:00\", \"total\": 45.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-12-08T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1935-12-15T00:00:00\", 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{\"date\": \"1936-05-24T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-05-31T00:00:00\", \"total\": 31.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-06-07T00:00:00\", \"total\": 31.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-06-14T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-06-21T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-06-28T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-07-05T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-07-12T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-07-19T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-07-26T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-08-02T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-08-09T00:00:00\", \"total\": 44.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-08-16T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-08-23T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-08-30T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-09-06T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-09-13T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-09-20T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-09-27T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-10-04T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-10-11T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-10-18T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-10-25T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-11-01T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-11-08T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-11-15T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-11-22T00:00:00\", \"total\": 31.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-11-29T00:00:00\", \"total\": 39.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-12-06T00:00:00\", \"total\": 43.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-12-13T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-12-20T00:00:00\", \"total\": 29.0, \"type\": \"Borrow event count\"}, {\"date\": \"1936-12-27T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-01-03T00:00:00\", \"total\": 29.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-01-10T00:00:00\", \"total\": 34.0, \"type\": \"Borrow event 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\"1937-04-04T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-04-11T00:00:00\", \"total\": 41.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-04-18T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-04-25T00:00:00\", \"total\": 38.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-05-02T00:00:00\", \"total\": 29.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-05-09T00:00:00\", \"total\": 36.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-05-16T00:00:00\", \"total\": 46.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-05-23T00:00:00\", \"total\": 43.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-05-30T00:00:00\", \"total\": 28.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-06-06T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-06-13T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-06-20T00:00:00\", 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\"1937-11-28T00:00:00\", \"total\": 40.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-12-05T00:00:00\", \"total\": 35.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-12-12T00:00:00\", \"total\": 58.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-12-19T00:00:00\", \"total\": 45.0, \"type\": \"Borrow event count\"}, {\"date\": \"1937-12-26T00:00:00\", \"total\": 34.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-01-02T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-01-09T00:00:00\", \"total\": 41.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-01-16T00:00:00\", \"total\": 49.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-01-23T00:00:00\", \"total\": 42.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-01-30T00:00:00\", \"total\": 44.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-02-06T00:00:00\", \"total\": 34.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-02-13T00:00:00\", 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\"Borrow event count\"}, {\"date\": \"1938-05-08T00:00:00\", \"total\": 46.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-05-15T00:00:00\", \"total\": 41.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-05-22T00:00:00\", \"total\": 41.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-05-29T00:00:00\", \"total\": 38.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-06-05T00:00:00\", \"total\": 44.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-06-12T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-06-19T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-06-26T00:00:00\", \"total\": 37.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-07-03T00:00:00\", \"total\": 39.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-07-10T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-07-17T00:00:00\", \"total\": 40.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-07-24T00:00:00\", \"total\": 15.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-07-31T00:00:00\", \"total\": 72.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-08-07T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-08-14T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-08-21T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-08-28T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-09-04T00:00:00\", \"total\": 1.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-09-11T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-09-18T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-09-25T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-10-02T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1938-10-09T00:00:00\", 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\"Borrow event count\"}, {\"date\": \"1939-01-01T00:00:00\", \"total\": 38.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-01-08T00:00:00\", \"total\": 42.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-01-15T00:00:00\", \"total\": 59.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-01-22T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-01-29T00:00:00\", \"total\": 68.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-02-05T00:00:00\", \"total\": 44.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-02-12T00:00:00\", \"total\": 46.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-02-19T00:00:00\", \"total\": 56.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-02-26T00:00:00\", \"total\": 46.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-03-05T00:00:00\", \"total\": 48.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-03-12T00:00:00\", \"total\": 48.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-03-19T00:00:00\", \"total\": 51.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-03-26T00:00:00\", \"total\": 42.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-04-02T00:00:00\", \"total\": 67.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-04-09T00:00:00\", \"total\": 58.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-04-16T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-04-23T00:00:00\", \"total\": 55.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-04-30T00:00:00\", \"total\": 57.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-05-07T00:00:00\", \"total\": 66.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-05-14T00:00:00\", \"total\": 39.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-05-21T00:00:00\", \"total\": 47.0, \"type\": \"Borrow event count\"}, {\"date\": \"1939-05-28T00:00:00\", \"total\": 48.0, \"type\": \"Borrow event count\"}, {\"date\": 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event count\"}, {\"date\": \"1940-07-07T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-07-14T00:00:00\", \"total\": 6.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-07-21T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-07-28T00:00:00\", \"total\": 10.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-08-04T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-08-11T00:00:00\", \"total\": 7.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-08-18T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-08-25T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-09-01T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-09-08T00:00:00\", \"total\": 9.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-09-15T00:00:00\", \"total\": 13.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-09-22T00:00:00\", \"total\": 16.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-09-29T00:00:00\", \"total\": 14.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-10-06T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-10-13T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-10-20T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-10-27T00:00:00\", \"total\": 26.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-11-03T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-11-10T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-11-17T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-11-24T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-12-01T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-12-08T00:00:00\", \"total\": 26.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-12-15T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-12-22T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1940-12-29T00:00:00\", \"total\": 12.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-01-05T00:00:00\", \"total\": 16.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-01-12T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-01-19T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-01-26T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-02-02T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-02-09T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-02-16T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-02-23T00:00:00\", \"total\": 16.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-03-02T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-03-09T00:00:00\", \"total\": 17.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-03-16T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-03-23T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-03-30T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-04-06T00:00:00\", \"total\": 19.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-04-13T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-04-20T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-04-27T00:00:00\", \"total\": 33.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-05-04T00:00:00\", \"total\": 29.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-05-11T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-05-18T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-05-25T00:00:00\", \"total\": 24.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-06-01T00:00:00\", \"total\": 38.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-06-08T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-06-15T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-06-22T00:00:00\", \"total\": 23.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-06-29T00:00:00\", \"total\": 21.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-07-06T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-07-13T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-07-20T00:00:00\", \"total\": 26.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-07-27T00:00:00\", \"total\": 29.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-08-03T00:00:00\", \"total\": 68.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-08-10T00:00:00\", \"total\": 8.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-08-17T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-08-24T00:00:00\", \"total\": 2.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-08-31T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-09-07T00:00:00\", \"total\": 20.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-09-14T00:00:00\", \"total\": 18.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-09-21T00:00:00\", \"total\": 25.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-09-28T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-10-05T00:00:00\", \"total\": 36.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-10-12T00:00:00\", \"total\": 30.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-10-19T00:00:00\", \"total\": 22.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-10-26T00:00:00\", \"total\": 36.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-11-02T00:00:00\", \"total\": 31.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-11-09T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-11-16T00:00:00\", \"total\": 35.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-11-23T00:00:00\", \"total\": 27.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-11-30T00:00:00\", \"total\": 36.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-12-07T00:00:00\", \"total\": 32.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-12-14T00:00:00\", \"total\": 34.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-12-21T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1941-12-28T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-01-04T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-01-11T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-01-18T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-01-25T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-02-01T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-02-08T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-02-15T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-02-22T00:00:00\", \"total\": 1.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-03-01T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-03-08T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-03-15T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-03-22T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-03-29T00:00:00\", \"total\": 5.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-04-05T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-04-12T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-04-19T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-04-26T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-05-03T00:00:00\", \"total\": 3.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-05-10T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-05-17T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-05-24T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-05-31T00:00:00\", \"total\": 6.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-06-07T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-06-14T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-06-21T00:00:00\", \"total\": 6.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-06-28T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-07-05T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-07-12T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-07-19T00:00:00\", \"total\": 3.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-07-26T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-08-02T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-08-09T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-08-16T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-08-23T00:00:00\", \"total\": 2.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-08-30T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1942-09-06T00:00:00\", \"total\": 0.0, \"type\": \"Borrow event count\"}, {\"date\": \"1919-11-23T00:00:00\", \"total\": 5.7860137950080395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1919-11-30T00:00:00\", \"total\": 12.857808433351199, \"type\": \"Estimated borrow events\"}, {\"date\": \"1919-12-07T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1919-12-14T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1919-12-21T00:00:00\", \"total\": 24.42983602336728, \"type\": \"Estimated borrow events\"}, {\"date\": \"1919-12-28T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-01-04T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-01-11T00:00:00\", \"total\": 30.215849818375318, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-01-18T00:00:00\", \"total\": 30.215849818375318, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-01-25T00:00:00\", \"total\": 34.07319234838068, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-02-01T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-02-08T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-02-15T00:00:00\", \"total\": 43.07365825172651, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-02-22T00:00:00\", \"total\": 46.931000781731875, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-02-29T00:00:00\", \"total\": 46.288110360064316, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-03-07T00:00:00\", \"total\": 45.64521993839676, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-03-14T00:00:00\", \"total\": 46.931000781731875, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-03-21T00:00:00\", \"total\": 45.64521993839676, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-03-28T00:00:00\", \"total\": 48.21678162506699, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-04-04T00:00:00\", \"total\": 52.71701457673991, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-04-11T00:00:00\", \"total\": 48.85967204673456, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-04-18T00:00:00\", \"total\": 50.14545289006968, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-04-25T00:00:00\", \"total\": 48.21678162506699, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-05-02T00:00:00\", \"total\": 46.931000781731875, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-05-09T00:00:00\", \"total\": 49.50256246840212, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-05-16T00:00:00\", \"total\": 48.85967204673456, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-05-23T00:00:00\", \"total\": 50.788343311737236, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-05-30T00:00:00\", \"total\": 46.931000781731875, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-06-06T00:00:00\", \"total\": 46.288110360064316, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-06-13T00:00:00\", \"total\": 48.21678162506699, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-06-20T00:00:00\", \"total\": 50.788343311737236, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-06-27T00:00:00\", \"total\": 49.50256246840212, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-07-04T00:00:00\", \"total\": 53.35990499840747, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-07-11T00:00:00\", \"total\": 53.35990499840747, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-07-18T00:00:00\", \"total\": 53.35990499840747, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-07-25T00:00:00\", \"total\": 51.431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-08-01T00:00:00\", \"total\": 46.288110360064316, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-08-08T00:00:00\", \"total\": 44.35943909506164, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-08-15T00:00:00\", \"total\": 39.21631572172116, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-08-22T00:00:00\", \"total\": 36.644754035050916, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-08-29T00:00:00\", \"total\": 32.787411505045554, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-09-05T00:00:00\", \"total\": 34.71608277004824, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-09-12T00:00:00\", \"total\": 36.644754035050916, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-09-19T00:00:00\", \"total\": 36.00186361338336, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-09-26T00:00:00\", \"total\": 36.00186361338336, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-10-03T00:00:00\", \"total\": 36.00186361338336, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-10-10T00:00:00\", \"total\": 37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-10-17T00:00:00\", \"total\": 43.07365825172651, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-10-24T00:00:00\", \"total\": 45.64521993839676, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-10-31T00:00:00\", \"total\": 47.573891203399434, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-11-07T00:00:00\", \"total\": 48.85967204673456, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-11-14T00:00:00\", \"total\": 61.717480480085754, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-11-21T00:00:00\", \"total\": 64.93193258842355, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-11-28T00:00:00\", \"total\": 69.43216554009648, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-12-05T00:00:00\", \"total\": 68.78927511842892, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-12-12T00:00:00\", \"total\": 69.43216554009648, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-12-19T00:00:00\", \"total\": 59.78880921508308, \"type\": \"Estimated borrow events\"}, {\"date\": \"1920-12-26T00:00:00\", \"total\": 63.64615174508843, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-01-02T00:00:00\", \"total\": 67.5034942750938, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-01-09T00:00:00\", \"total\": 68.14638469676136, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-01-16T00:00:00\", \"total\": 67.5034942750938, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-01-23T00:00:00\", \"total\": 70.07505596176404, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-01-30T00:00:00\", \"total\": 69.43216554009648, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-02-06T00:00:00\", \"total\": 70.07505596176404, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-02-13T00:00:00\", \"total\": 72.00372722676671, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-02-20T00:00:00\", \"total\": 80.361302708445, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-02-27T00:00:00\", \"total\": 82.93286439511523, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-03-06T00:00:00\", \"total\": 80.361302708445, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-03-13T00:00:00\", \"total\": 84.21864523845035, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-03-20T00:00:00\", \"total\": 82.28997397344767, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-03-27T00:00:00\", \"total\": 79.71841228677744, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-04-03T00:00:00\", \"total\": 78.43263144344232, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-04-10T00:00:00\", \"total\": 85.50442608178547, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-04-17T00:00:00\", \"total\": 86.14731650345303, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-04-24T00:00:00\", \"total\": 89.36176861179084, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-05-01T00:00:00\", \"total\": 91.93333029846107, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-05-08T00:00:00\", \"total\": 91.29043987679351, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-05-15T00:00:00\", \"total\": 88.71887819012328, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-05-22T00:00:00\", \"total\": 86.14731650345303, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-05-29T00:00:00\", \"total\": 86.79020692512059, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-06-05T00:00:00\", \"total\": 86.14731650345303, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-06-12T00:00:00\", \"total\": 80.361302708445, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-06-19T00:00:00\", \"total\": 77.78974102177476, \"type\": \"Estimated borrow events\"}, {\"date\": \"1921-06-26T00:00:00\", 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{\"date\": \"1929-03-10T00:00:00\", \"total\": 113.14871421349055, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-03-17T00:00:00\", \"total\": 118.29183758683104, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-03-24T00:00:00\", \"total\": 117.64894716516346, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-03-31T00:00:00\", \"total\": 118.9347280084986, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-04-07T00:00:00\", \"total\": 120.86339927350127, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-04-14T00:00:00\", \"total\": 120.22050885183371, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-04-21T00:00:00\", \"total\": 116.36316632182834, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-04-28T00:00:00\", \"total\": 122.79207053850395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-05-05T00:00:00\", \"total\": 126.00652264684174, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-05-12T00:00:00\", 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\"Estimated borrow events\"}, {\"date\": \"1929-07-21T00:00:00\", \"total\": 77.1468506001072, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-07-28T00:00:00\", \"total\": 73.28950807010183, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-08-04T00:00:00\", \"total\": 75.86106975677207, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-08-11T00:00:00\", \"total\": 74.57528891343695, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-08-18T00:00:00\", \"total\": 70.7179463834316, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-08-25T00:00:00\", \"total\": 74.57528891343695, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-09-01T00:00:00\", \"total\": 72.64661764843427, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-09-08T00:00:00\", \"total\": 70.7179463834316, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-09-15T00:00:00\", \"total\": 71.36083680509915, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-09-22T00:00:00\", \"total\": 70.7179463834316, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-09-29T00:00:00\", \"total\": 75.21817933510451, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-10-06T00:00:00\", \"total\": 82.28997397344767, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-10-13T00:00:00\", \"total\": 83.57575481678279, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-10-20T00:00:00\", \"total\": 86.14731650345303, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-10-27T00:00:00\", \"total\": 90.0046590334584, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-11-03T00:00:00\", \"total\": 88.71887819012328, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-11-10T00:00:00\", \"total\": 91.29043987679351, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-11-17T00:00:00\", \"total\": 93.21911114179619, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-11-24T00:00:00\", \"total\": 91.93333029846107, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-12-01T00:00:00\", \"total\": 91.29043987679351, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-12-08T00:00:00\", \"total\": 95.14778240679887, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-12-15T00:00:00\", \"total\": 92.57622072012863, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-12-22T00:00:00\", \"total\": 86.14731650345303, \"type\": \"Estimated borrow events\"}, {\"date\": \"1929-12-29T00:00:00\", \"total\": 88.71887819012328, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-01-05T00:00:00\", \"total\": 86.14731650345303, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-01-12T00:00:00\", \"total\": 71.36083680509915, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-01-19T00:00:00\", \"total\": 68.14638469676136, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-01-26T00:00:00\", \"total\": 59.78880921508308, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-02-02T00:00:00\", \"total\": 53.35990499840747, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-02-09T00:00:00\", \"total\": 49.50256246840212, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-02-16T00:00:00\", \"total\": 48.21678162506699, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-02-23T00:00:00\", \"total\": 43.07365825172651, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-03-02T00:00:00\", \"total\": 39.85920614338872, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-03-09T00:00:00\", \"total\": 33.43030192671311, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-03-16T00:00:00\", \"total\": 30.858740240042877, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-03-23T00:00:00\", \"total\": 30.858740240042877, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-03-30T00:00:00\", \"total\": 30.215849818375318, \"type\": \"Estimated borrow events\"}, {\"date\": 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11.572027590016079, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-06-15T00:00:00\", \"total\": 14.78647969835388, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-06-22T00:00:00\", \"total\": 15.429370120021439, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-06-29T00:00:00\", \"total\": 15.429370120021439, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-07-06T00:00:00\", \"total\": 15.429370120021439, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-07-13T00:00:00\", \"total\": 14.78647969835388, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-07-20T00:00:00\", \"total\": 12.21491801168364, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-07-27T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-08-03T00:00:00\", \"total\": 16.715150963356557, \"type\": \"Estimated borrow events\"}, {\"date\": \"1930-08-10T00:00:00\", \"total\": 13.500698855018758, \"type\": 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37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-05-14T00:00:00\", \"total\": 42.430767830058954, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-05-21T00:00:00\", \"total\": 46.931000781731875, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-05-28T00:00:00\", \"total\": 46.288110360064316, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-06-04T00:00:00\", \"total\": 45.64521993839676, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-06-11T00:00:00\", \"total\": 39.21631572172116, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-06-18T00:00:00\", \"total\": 34.71608277004824, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-06-25T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-07-02T00:00:00\", \"total\": 27.001397710037516, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-07-09T00:00:00\", \"total\": 26.358507288369957, \"type\": 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37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-11-26T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-12-03T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-12-10T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-12-17T00:00:00\", \"total\": 41.787877408391395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-12-24T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1933-12-31T00:00:00\", \"total\": 42.430767830058954, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-01-07T00:00:00\", \"total\": 39.85920614338872, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-01-14T00:00:00\", \"total\": 38.5734253000536, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-01-21T00:00:00\", \"total\": 37.930534878386034, \"type\": \"Estimated 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13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-06-10T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-06-17T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-06-24T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-07-01T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-07-08T00:00:00\", \"total\": 18.643822228359237, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-07-15T00:00:00\", \"total\": 18.643822228359237, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-07-22T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-07-29T00:00:00\", \"total\": 14.78647969835388, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-08-05T00:00:00\", \"total\": 9.6433563250134, \"type\": \"Estimated 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7.071794638343159, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-12-23T00:00:00\", \"total\": 7.071794638343159, \"type\": \"Estimated borrow events\"}, {\"date\": \"1934-12-30T00:00:00\", \"total\": 7.071794638343159, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-01-06T00:00:00\", \"total\": 6.428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-01-13T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-01-20T00:00:00\", \"total\": 5.7860137950080395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-01-27T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-02-03T00:00:00\", \"total\": 12.857808433351199, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-02-10T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-02-17T00:00:00\", \"total\": 16.715150963356557, \"type\": \"Estimated 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23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-07-07T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-07-14T00:00:00\", \"total\": 24.42983602336728, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-07-21T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-07-28T00:00:00\", \"total\": 20.572493493361918, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-08-04T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-08-11T00:00:00\", \"total\": 17.35804138502412, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-08-18T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-08-25T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-09-01T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-09-08T00:00:00\", \"total\": 17.35804138502412, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-09-15T00:00:00\", \"total\": 15.429370120021439, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-09-22T00:00:00\", \"total\": 12.857808433351199, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-09-29T00:00:00\", \"total\": 12.21491801168364, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-10-06T00:00:00\", \"total\": 8.357575481678278, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-10-13T00:00:00\", \"total\": 9.00046590334584, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-10-20T00:00:00\", \"total\": 12.857808433351199, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-10-27T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1935-11-03T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": 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27.001397710037516, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-01-19T00:00:00\", \"total\": 32.144521083377995, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-01-26T00:00:00\", \"total\": 30.858740240042877, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-02-02T00:00:00\", \"total\": 37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-02-09T00:00:00\", \"total\": 38.5734253000536, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-02-16T00:00:00\", \"total\": 39.85920614338872, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-02-23T00:00:00\", \"total\": 41.144986986723836, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-03-01T00:00:00\", \"total\": 42.430767830058954, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-03-08T00:00:00\", \"total\": 43.07365825172651, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-03-15T00:00:00\", \"total\": 45.0023295167292, \"type\": \"Estimated 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\"1936-05-24T00:00:00\", \"total\": 37.930534878386034, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-05-31T00:00:00\", \"total\": 37.930534878386034, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-06-07T00:00:00\", \"total\": 37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-06-14T00:00:00\", \"total\": 35.3589731917158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-06-21T00:00:00\", \"total\": 31.501630661710436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-06-28T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-07-05T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-07-12T00:00:00\", \"total\": 25.715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-07-19T00:00:00\", \"total\": 25.715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-07-26T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-08-02T00:00:00\", \"total\": 24.42983602336728, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-08-09T00:00:00\", \"total\": 25.715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-08-16T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-08-23T00:00:00\", \"total\": 20.572493493361918, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-08-30T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-09-06T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-09-13T00:00:00\", \"total\": 22.5011647583646, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-09-20T00:00:00\", \"total\": 22.5011647583646, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-09-27T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-10-04T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-10-11T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-10-18T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-10-25T00:00:00\", \"total\": 25.715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-11-01T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-11-08T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-11-15T00:00:00\", \"total\": 25.07272644503484, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-11-22T00:00:00\", \"total\": 30.858740240042877, \"type\": \"Estimated borrow events\"}, {\"date\": \"1936-11-29T00:00:00\", \"total\": 32.787411505045554, \"type\": \"Estimated borrow events\"}, {\"date\": 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25.07272644503484, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-02-14T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-02-21T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-02-28T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-03-07T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-03-14T00:00:00\", \"total\": 22.5011647583646, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-03-21T00:00:00\", \"total\": 25.07272644503484, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-03-28T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-04-04T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-04-11T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-04-18T00:00:00\", \"total\": 22.5011647583646, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-04-25T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-05-02T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-05-09T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-05-16T00:00:00\", \"total\": 20.572493493361918, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-05-23T00:00:00\", \"total\": 17.35804138502412, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-05-30T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-06-06T00:00:00\", \"total\": 12.21491801168364, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-06-13T00:00:00\", \"total\": 10.929137168348518, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-06-20T00:00:00\", \"total\": 9.6433563250134, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-06-27T00:00:00\", \"total\": 9.00046590334584, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-07-04T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-07-11T00:00:00\", \"total\": 9.6433563250134, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-07-18T00:00:00\", \"total\": 7.714685060010719, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-07-25T00:00:00\", \"total\": 7.071794638343159, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-08-01T00:00:00\", \"total\": 5.7860137950080395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-08-08T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-08-15T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-08-22T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-08-29T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-09-05T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-09-12T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-09-19T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-09-26T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-10-03T00:00:00\", \"total\": 1.9286712650026798, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-10-10T00:00:00\", \"total\": 1.2857808433351199, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-10-17T00:00:00\", \"total\": 1.2857808433351199, \"type\": \"Estimated borrow events\"}, {\"date\": \"1937-10-24T00:00:00\", \"total\": 1.2857808433351199, \"type\": 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{\"date\": \"1938-01-02T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-01-09T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-01-16T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-01-23T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-01-30T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-02-06T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-02-13T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-02-20T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-02-27T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-03-06T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-03-13T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-03-20T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-03-27T00:00:00\", \"total\": 7.071794638343159, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-04-03T00:00:00\", \"total\": 9.00046590334584, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-04-10T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-04-17T00:00:00\", \"total\": 11.572027590016079, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-04-24T00:00:00\", \"total\": 10.929137168348518, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-05-01T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-05-08T00:00:00\", \"total\": 7.714685060010719, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-05-15T00:00:00\", \"total\": 5.7860137950080395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-05-22T00:00:00\", \"total\": 5.7860137950080395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-05-29T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-06-05T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-06-12T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-06-19T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-06-26T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-07-03T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-07-10T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": 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3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-09-25T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-10-02T00:00:00\", \"total\": 3.2144521083377997, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-10-09T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-10-16T00:00:00\", \"total\": 1.9286712650026798, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-10-23T00:00:00\", \"total\": 1.9286712650026798, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-10-30T00:00:00\", \"total\": 6.428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-11-06T00:00:00\", \"total\": 14.78647969835388, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-11-13T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1938-11-20T00:00:00\", \"total\": 25.715616866702398, \"type\": 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28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-04-09T00:00:00\", \"total\": 32.144521083377995, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-04-16T00:00:00\", \"total\": 30.215849818375318, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-04-23T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-04-30T00:00:00\", \"total\": 24.42983602336728, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-05-07T00:00:00\", \"total\": 27.001397710037516, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-05-14T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-05-21T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-05-28T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-06-04T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-06-11T00:00:00\", \"total\": 20.572493493361918, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-06-18T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-06-25T00:00:00\", \"total\": 27.001397710037516, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-07-02T00:00:00\", \"total\": 27.64428813170508, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-07-09T00:00:00\", \"total\": 27.64428813170508, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-07-16T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-07-23T00:00:00\", \"total\": 25.07272644503484, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-07-30T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-08-06T00:00:00\", \"total\": 20.572493493361918, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-08-13T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-08-20T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-08-27T00:00:00\", \"total\": 12.21491801168364, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-09-03T00:00:00\", \"total\": 10.929137168348518, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-09-10T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-09-17T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-09-24T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-10-01T00:00:00\", \"total\": 9.6433563250134, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-10-08T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-10-15T00:00:00\", \"total\": 9.6433563250134, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-10-22T00:00:00\", \"total\": 12.21491801168364, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-10-29T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-11-05T00:00:00\", \"total\": 14.78647969835388, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-11-12T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-11-19T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-11-26T00:00:00\", \"total\": 17.35804138502412, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-12-03T00:00:00\", \"total\": 18.00093180669168, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-12-10T00:00:00\", \"total\": 19.2867126500268, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-12-17T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-12-24T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1939-12-31T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-01-07T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-01-14T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-01-21T00:00:00\", \"total\": 24.42983602336728, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-01-28T00:00:00\", \"total\": 26.358507288369957, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-02-04T00:00:00\", \"total\": 26.358507288369957, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-02-11T00:00:00\", \"total\": 27.001397710037516, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-02-18T00:00:00\", \"total\": 26.358507288369957, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-02-25T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-03-03T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-03-10T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-03-17T00:00:00\", \"total\": 30.215849818375318, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-03-24T00:00:00\", \"total\": 30.858740240042877, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-03-31T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-04-07T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-04-14T00:00:00\", \"total\": 27.001397710037516, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-04-21T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-04-28T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-05-05T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-05-12T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-05-19T00:00:00\", \"total\": 23.786945601699717, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-05-26T00:00:00\", \"total\": 22.5011647583646, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-06-02T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-06-09T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-06-16T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-06-23T00:00:00\", \"total\": 15.429370120021439, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-06-30T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-07-07T00:00:00\", \"total\": 12.21491801168364, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-07-14T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-07-21T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-07-28T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-08-04T00:00:00\", \"total\": 11.572027590016079, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-08-11T00:00:00\", \"total\": 10.929137168348518, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-08-18T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-08-25T00:00:00\", \"total\": 14.78647969835388, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-09-01T00:00:00\", \"total\": 15.429370120021439, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-09-08T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-09-15T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-09-22T00:00:00\", \"total\": 14.78647969835388, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-09-29T00:00:00\", \"total\": 16.715150963356557, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-10-06T00:00:00\", \"total\": 18.00093180669168, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-10-13T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-10-20T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-10-27T00:00:00\", \"total\": 26.358507288369957, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-11-03T00:00:00\", \"total\": 25.07272644503484, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-11-10T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-11-17T00:00:00\", \"total\": 28.287178553372637, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-11-24T00:00:00\", \"total\": 29.57295939670776, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-12-01T00:00:00\", \"total\": 30.215849818375318, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-12-08T00:00:00\", \"total\": 31.501630661710436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-12-15T00:00:00\", \"total\": 31.501630661710436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-12-22T00:00:00\", \"total\": 35.3589731917158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1940-12-29T00:00:00\", \"total\": 34.71608277004824, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-01-05T00:00:00\", \"total\": 36.00186361338336, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-01-12T00:00:00\", \"total\": 36.644754035050916, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-01-19T00:00:00\", \"total\": 38.5734253000536, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-01-26T00:00:00\", \"total\": 34.71608277004824, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-02-02T00:00:00\", \"total\": 34.71608277004824, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-02-09T00:00:00\", \"total\": 34.71608277004824, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-02-16T00:00:00\", \"total\": 34.71608277004824, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-02-23T00:00:00\", \"total\": 37.930534878386034, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-03-02T00:00:00\", \"total\": 37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-03-09T00:00:00\", \"total\": 41.144986986723836, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-03-16T00:00:00\", \"total\": 42.430767830058954, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-03-23T00:00:00\", \"total\": 41.144986986723836, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-03-30T00:00:00\", \"total\": 38.5734253000536, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-04-06T00:00:00\", \"total\": 43.07365825172651, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-04-13T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-04-20T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-04-27T00:00:00\", \"total\": 42.430767830058954, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-05-04T00:00:00\", \"total\": 43.07365825172651, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-05-11T00:00:00\", \"total\": 40.50209656505628, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-05-18T00:00:00\", \"total\": 41.144986986723836, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-05-25T00:00:00\", \"total\": 37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-06-01T00:00:00\", \"total\": 39.85920614338872, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-06-08T00:00:00\", \"total\": 42.430767830058954, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-06-15T00:00:00\", \"total\": 41.144986986723836, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-06-22T00:00:00\", \"total\": 37.930534878386034, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-06-29T00:00:00\", \"total\": 37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-07-06T00:00:00\", \"total\": 35.3589731917158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-07-13T00:00:00\", \"total\": 37.930534878386034, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-07-20T00:00:00\", \"total\": 38.5734253000536, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-07-27T00:00:00\", \"total\": 37.287644456718475, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-08-03T00:00:00\", \"total\": 33.43030192671311, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-08-10T00:00:00\", \"total\": 30.858740240042877, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-08-17T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-08-24T00:00:00\", \"total\": 23.144055180032158, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-08-31T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-09-07T00:00:00\", \"total\": 18.643822228359237, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-09-14T00:00:00\", \"total\": 19.2867126500268, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-09-21T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-09-28T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-10-05T00:00:00\", \"total\": 32.144521083377995, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-10-12T00:00:00\", \"total\": 33.43030192671311, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-10-19T00:00:00\", \"total\": 45.0023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-10-26T00:00:00\", \"total\": 50.788343311737236, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-11-02T00:00:00\", \"total\": 52.074124155072354, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-11-09T00:00:00\", \"total\": 57.217247528412834, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-11-16T00:00:00\", \"total\": 61.717480480085754, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-11-23T00:00:00\", \"total\": 61.074590058418195, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-11-30T00:00:00\", \"total\": 59.78880921508308, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-12-07T00:00:00\", \"total\": 59.14591879341552, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-12-14T00:00:00\", \"total\": 61.074590058418195, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-12-21T00:00:00\", \"total\": 55.28857626341016, \"type\": \"Estimated borrow events\"}, {\"date\": \"1941-12-28T00:00:00\", \"total\": 47.573891203399434, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-01-04T00:00:00\", \"total\": 38.5734253000536, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-01-11T00:00:00\", \"total\": 32.787411505045554, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-01-18T00:00:00\", \"total\": 26.358507288369957, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-01-25T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-02-01T00:00:00\", \"total\": 17.35804138502412, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-02-08T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-02-15T00:00:00\", \"total\": 10.929137168348518, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-02-22T00:00:00\", \"total\": 9.00046590334584, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-03-01T00:00:00\", \"total\": 9.00046590334584, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-03-08T00:00:00\", \"total\": 7.714685060010719, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-03-15T00:00:00\", \"total\": 5.7860137950080395, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-03-22T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-03-29T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-04-05T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-04-12T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-04-19T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-04-26T00:00:00\", \"total\": 1.2857808433351199, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-05-03T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-05-10T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-05-17T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-05-24T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-05-31T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-06-07T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-06-14T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-06-21T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-06-28T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-07-05T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-07-12T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-07-19T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-07-26T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-08-02T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-08-09T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-08-16T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-08-23T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-08-30T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1942-09-06T00:00:00\", \"total\": 0.0, \"type\": \"Estimated borrow events\"}, {\"date\": \"1919-11-23T00:00:00\", \"total\": 12.786013795008039, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1919-11-30T00:00:00\", \"total\": 17.8578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1919-12-07T00:00:00\", \"total\": 28.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1919-12-14T00:00:00\", \"total\": 28.786945601699717, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1919-12-21T00:00:00\", \"total\": 31.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1919-12-28T00:00:00\", \"total\": 33.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-01-04T00:00:00\", \"total\": 33.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-01-11T00:00:00\", \"total\": 39.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-01-18T00:00:00\", \"total\": 34.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-01-25T00:00:00\", \"total\": 39.07319234838068, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-02-01T00:00:00\", \"total\": 41.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-02-08T00:00:00\", \"total\": 43.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-02-15T00:00:00\", \"total\": 45.07365825172651, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-02-22T00:00:00\", \"total\": 52.931000781731875, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-02-29T00:00:00\", \"total\": 49.288110360064316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-03-07T00:00:00\", \"total\": 52.64521993839676, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-03-14T00:00:00\", \"total\": 49.931000781731875, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-03-21T00:00:00\", \"total\": 48.64521993839676, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-03-28T00:00:00\", \"total\": 51.21678162506699, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-04-04T00:00:00\", \"total\": 58.71701457673991, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-04-11T00:00:00\", \"total\": 51.85967204673456, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-04-18T00:00:00\", \"total\": 62.14545289006968, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-04-25T00:00:00\", \"total\": 50.21678162506699, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-05-02T00:00:00\", \"total\": 50.931000781731875, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-05-09T00:00:00\", \"total\": 55.50256246840212, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-05-16T00:00:00\", \"total\": 55.85967204673456, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-05-23T00:00:00\", \"total\": 53.788343311737236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-05-30T00:00:00\", \"total\": 52.931000781731875, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-06-06T00:00:00\", \"total\": 49.288110360064316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-06-13T00:00:00\", \"total\": 50.21678162506699, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-06-20T00:00:00\", \"total\": 54.788343311737236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-06-27T00:00:00\", \"total\": 52.50256246840212, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-07-04T00:00:00\", \"total\": 57.35990499840747, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-07-11T00:00:00\", \"total\": 60.35990499840747, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-07-18T00:00:00\", \"total\": 57.35990499840747, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-07-25T00:00:00\", \"total\": 51.431233733404795, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-08-01T00:00:00\", \"total\": 54.288110360064316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-08-08T00:00:00\", \"total\": 44.35943909506164, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-08-15T00:00:00\", \"total\": 39.21631572172116, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-08-22T00:00:00\", \"total\": 36.644754035050916, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-08-29T00:00:00\", \"total\": 32.787411505045554, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-09-05T00:00:00\", \"total\": 37.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-09-12T00:00:00\", \"total\": 37.644754035050916, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-09-19T00:00:00\", \"total\": 37.00186361338336, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-09-26T00:00:00\", \"total\": 38.00186361338336, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-10-03T00:00:00\", \"total\": 40.00186361338336, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-10-10T00:00:00\", \"total\": 39.287644456718475, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-10-17T00:00:00\", \"total\": 51.07365825172651, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-10-24T00:00:00\", \"total\": 48.64521993839676, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-10-31T00:00:00\", \"total\": 54.573891203399434, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-11-07T00:00:00\", \"total\": 55.85967204673456, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-11-14T00:00:00\", \"total\": 73.71748048008575, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-11-21T00:00:00\", \"total\": 70.93193258842355, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-11-28T00:00:00\", \"total\": 84.43216554009648, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-12-05T00:00:00\", \"total\": 80.78927511842892, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-12-12T00:00:00\", \"total\": 79.43216554009648, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-12-19T00:00:00\", \"total\": 67.78880921508308, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1920-12-26T00:00:00\", \"total\": 69.64615174508843, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-01-02T00:00:00\", \"total\": 69.5034942750938, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-01-09T00:00:00\", \"total\": 76.14638469676136, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-01-16T00:00:00\", \"total\": 73.5034942750938, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-01-23T00:00:00\", \"total\": 73.07505596176404, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-01-30T00:00:00\", \"total\": 75.43216554009648, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-02-06T00:00:00\", \"total\": 83.07505596176404, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-02-13T00:00:00\", \"total\": 78.00372722676671, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-02-20T00:00:00\", \"total\": 87.361302708445, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-02-27T00:00:00\", \"total\": 88.93286439511523, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-03-06T00:00:00\", \"total\": 87.361302708445, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-03-13T00:00:00\", \"total\": 91.21864523845035, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-03-20T00:00:00\", \"total\": 88.28997397344767, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-03-27T00:00:00\", \"total\": 82.71841228677744, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-04-03T00:00:00\", \"total\": 82.43263144344232, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-04-10T00:00:00\", \"total\": 92.50442608178547, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-04-17T00:00:00\", \"total\": 94.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-04-24T00:00:00\", \"total\": 98.36176861179084, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-05-01T00:00:00\", \"total\": 97.93333029846107, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-05-08T00:00:00\", \"total\": 95.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-05-15T00:00:00\", \"total\": 93.71887819012328, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-05-22T00:00:00\", \"total\": 90.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-05-29T00:00:00\", \"total\": 91.79020692512059, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-06-05T00:00:00\", \"total\": 95.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-06-12T00:00:00\", \"total\": 91.361302708445, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-06-19T00:00:00\", \"total\": 83.78974102177476, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-06-26T00:00:00\", \"total\": 85.1468506001072, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-07-03T00:00:00\", \"total\": 79.50396017843963, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-07-10T00:00:00\", \"total\": 79.86106975677207, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-07-17T00:00:00\", \"total\": 79.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-07-24T00:00:00\", \"total\": 70.07505596176404, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-07-31T00:00:00\", \"total\": 77.57482301009111, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-08-07T00:00:00\", \"total\": 76.07505596176404, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-08-14T00:00:00\", \"total\": 74.7179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-08-21T00:00:00\", \"total\": 75.5034942750938, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-08-28T00:00:00\", \"total\": 74.14638469676136, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-09-04T00:00:00\", \"total\": 72.78927511842892, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-09-11T00:00:00\", \"total\": 74.07505596176404, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-09-18T00:00:00\", \"total\": 80.21817933510451, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-09-25T00:00:00\", \"total\": 82.07552186510988, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-10-02T00:00:00\", \"total\": 87.64708355178011, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-10-09T00:00:00\", \"total\": 89.64708355178011, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-10-16T00:00:00\", \"total\": 94.43309734678814, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-10-23T00:00:00\", \"total\": 92.07598776845572, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-10-30T00:00:00\", \"total\": 96.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-11-06T00:00:00\", \"total\": 103.57668662347447, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-11-13T00:00:00\", \"total\": 111.57668662347447, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-11-20T00:00:00\", \"total\": 107.21957704514203, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-11-27T00:00:00\", \"total\": 107.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-12-04T00:00:00\", \"total\": 108.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-12-11T00:00:00\", \"total\": 106.07645367180154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-12-18T00:00:00\", \"total\": 116.50535788847715, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1921-12-25T00:00:00\", \"total\": 115.43402915347983, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-01-01T00:00:00\", \"total\": 117.93426210515275, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-01-08T00:00:00\", \"total\": 128.07738547849323, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-01-15T00:00:00\", \"total\": 127.0060567434959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-01-22T00:00:00\", \"total\": 128.43449505682565, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-01-29T00:00:00\", \"total\": 132.36316632182834, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-02-05T00:00:00\", \"total\": 126.0060567434959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-02-12T00:00:00\", \"total\": 121.57715252682031, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-02-19T00:00:00\", \"total\": 131.14871421349056, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-02-26T00:00:00\", \"total\": 119.07738547849323, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-03-05T00:00:00\", \"total\": 123.43449505682567, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-03-12T00:00:00\", \"total\": 115.86293337015543, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-03-19T00:00:00\", \"total\": 113.86293337015543, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-03-26T00:00:00\", \"total\": 110.71980999681494, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-04-02T00:00:00\", \"total\": 104.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-04-09T00:00:00\", \"total\": 115.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-04-16T00:00:00\", \"total\": 106.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-04-23T00:00:00\", \"total\": 107.57668662347447, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-04-30T00:00:00\", \"total\": 112.14824831014471, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-05-07T00:00:00\", \"total\": 111.57668662347447, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-05-14T00:00:00\", \"total\": 103.6480153584718, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-05-21T00:00:00\", \"total\": 104.71934409346912, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-05-28T00:00:00\", \"total\": 105.36223451513668, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-06-04T00:00:00\", \"total\": 100.71934409346912, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-06-11T00:00:00\", \"total\": 101.21911114179619, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-06-18T00:00:00\", \"total\": 100.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-06-25T00:00:00\", \"total\": 98.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-07-02T00:00:00\", \"total\": 99.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-07-09T00:00:00\", \"total\": 103.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-07-16T00:00:00\", \"total\": 108.21957704514203, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-07-23T00:00:00\", \"total\": 103.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-07-30T00:00:00\", \"total\": 103.36223451513668, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-08-06T00:00:00\", \"total\": 104.50489198513131, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-08-13T00:00:00\", \"total\": 87.79020692512059, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-08-20T00:00:00\", \"total\": 83.28997397344767, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-08-27T00:00:00\", \"total\": 83.00419313011255, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-09-03T00:00:00\", \"total\": 81.361302708445, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-09-10T00:00:00\", \"total\": 97.64754945512595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-09-17T00:00:00\", \"total\": 98.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-09-24T00:00:00\", \"total\": 94.93333029846107, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-10-01T00:00:00\", \"total\": 97.21911114179619, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-10-08T00:00:00\", \"total\": 103.93333029846107, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-10-15T00:00:00\", \"total\": 103.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-10-22T00:00:00\", \"total\": 99.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-10-29T00:00:00\", \"total\": 101.71934409346912, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-11-05T00:00:00\", \"total\": 117.93426210515275, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-11-12T00:00:00\", \"total\": 121.79160463515811, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-11-19T00:00:00\", \"total\": 136.1491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-11-26T00:00:00\", \"total\": 138.00652264684174, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-12-03T00:00:00\", \"total\": 138.1491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-12-10T00:00:00\", \"total\": 142.64941306850932, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-12-17T00:00:00\", \"total\": 151.64987897185514, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-12-24T00:00:00\", \"total\": 153.57855023685784, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1922-12-31T00:00:00\", \"total\": 147.57855023685784, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-01-07T00:00:00\", \"total\": 143.36409812852003, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-01-14T00:00:00\", \"total\": 141.22097475517955, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-01-21T00:00:00\", \"total\": 137.43542686351734, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-01-28T00:00:00\", \"total\": 152.7925364418498, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-02-04T00:00:00\", \"total\": 144.22097475517955, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-02-11T00:00:00\", \"total\": 158.72120770685245, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-02-18T00:00:00\", \"total\": 147.72120770685245, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-02-25T00:00:00\", \"total\": 133.36363222517417, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-03-04T00:00:00\", \"total\": 142.07785138183908, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-03-11T00:00:00\", \"total\": 129.50628969516885, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-03-18T00:00:00\", \"total\": 129.86339927350127, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-03-25T00:00:00\", \"total\": 127.29183758683104, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-04-01T00:00:00\", \"total\": 128.64894716516346, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-04-08T00:00:00\", \"total\": 123.64894716516346, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-04-15T00:00:00\", \"total\": 129.57761843016615, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-04-22T00:00:00\", \"total\": 120.43449505682567, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-04-29T00:00:00\", \"total\": 119.57715252682031, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-05-06T00:00:00\", \"total\": 112.07691957514739, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-05-13T00:00:00\", \"total\": 106.07645367180154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-05-20T00:00:00\", \"total\": 114.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-05-27T00:00:00\", \"total\": 110.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-06-03T00:00:00\", \"total\": 119.64848126181764, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-06-10T00:00:00\", \"total\": 111.00559084015006, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-06-17T00:00:00\", \"total\": 112.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-06-24T00:00:00\", \"total\": 104.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-07-01T00:00:00\", \"total\": 103.6480153584718, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-07-08T00:00:00\", \"total\": 110.6480153584718, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-07-15T00:00:00\", \"total\": 109.00512493680424, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-07-22T00:00:00\", \"total\": 99.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-07-29T00:00:00\", \"total\": 95.0046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-08-05T00:00:00\", \"total\": 96.36176861179084, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-08-12T00:00:00\", \"total\": 93.79020692512059, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-08-19T00:00:00\", \"total\": 89.50442608178547, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-08-26T00:00:00\", \"total\": 87.361302708445, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-09-02T00:00:00\", \"total\": 86.43263144344232, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-09-09T00:00:00\", \"total\": 98.361302708445, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-09-16T00:00:00\", \"total\": 97.86153566011791, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-09-23T00:00:00\", \"total\": 117.86153566011791, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-09-30T00:00:00\", \"total\": 112.79067282846643, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-10-07T00:00:00\", \"total\": 104.21911114179619, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-10-14T00:00:00\", \"total\": 116.00512493680424, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-10-21T00:00:00\", \"total\": 122.07691957514739, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-10-28T00:00:00\", \"total\": 126.14871421349055, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-11-04T00:00:00\", \"total\": 130.79207053850394, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-11-11T00:00:00\", \"total\": 144.57808433351198, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-11-18T00:00:00\", \"total\": 145.93565981519026, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-11-25T00:00:00\", \"total\": 149.22144065852538, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-12-02T00:00:00\", \"total\": 149.5072215018605, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-12-09T00:00:00\", \"total\": 147.15011192352807, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-12-16T00:00:00\", \"total\": 151.5072215018605, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-12-23T00:00:00\", \"total\": 147.86433108019295, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1923-12-30T00:00:00\", \"total\": 150.00745445353343, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-01-06T00:00:00\", \"total\": 151.79300234519562, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-01-13T00:00:00\", \"total\": 153.07878318853074, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-01-20T00:00:00\", \"total\": 159.43635867020902, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-01-27T00:00:00\", \"total\": 159.93659162188195, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-02-03T00:00:00\", \"total\": 171.2937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-02-10T00:00:00\", \"total\": 171.22237246521706, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-02-17T00:00:00\", \"total\": 164.2937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-02-24T00:00:00\", \"total\": 148.650344875201, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-03-02T00:00:00\", \"total\": 152.36456403186585, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-03-09T00:00:00\", \"total\": 151.07878318853074, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-03-16T00:00:00\", \"total\": 154.79300234519562, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-03-23T00:00:00\", \"total\": 152.07878318853074, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-03-30T00:00:00\", \"total\": 158.36456403186585, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-04-06T00:00:00\", \"total\": 156.36456403186585, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-04-13T00:00:00\", \"total\": 161.07878318853074, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-04-20T00:00:00\", \"total\": 150.4358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-04-27T00:00:00\", \"total\": 147.86433108019295, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-05-04T00:00:00\", \"total\": 145.93565981519026, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-05-11T00:00:00\", \"total\": 145.29276939352272, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-05-18T00:00:00\", \"total\": 134.36363222517417, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-05-25T00:00:00\", \"total\": 129.2205088518337, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-06-01T00:00:00\", \"total\": 132.1491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-06-08T00:00:00\", \"total\": 120.72027590016079, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-06-15T00:00:00\", \"total\": 122.43449505682567, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-06-22T00:00:00\", \"total\": 130.57761843016615, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-06-29T00:00:00\", \"total\": 126.86293337015543, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-07-06T00:00:00\", \"total\": 118.07691957514739, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-07-13T00:00:00\", \"total\": 117.71980999681494, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-07-20T00:00:00\", \"total\": 121.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-07-27T00:00:00\", \"total\": 118.2913716834852, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-08-03T00:00:00\", \"total\": 127.50582379182299, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-08-10T00:00:00\", \"total\": 122.86293337015543, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-08-17T00:00:00\", \"total\": 114.00559084015006, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-08-24T00:00:00\", \"total\": 105.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-08-31T00:00:00\", \"total\": 109.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-09-07T00:00:00\", \"total\": 112.6480153584718, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-09-14T00:00:00\", \"total\": 113.64848126181764, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-09-21T00:00:00\", \"total\": 118.00559084015006, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-09-28T00:00:00\", \"total\": 121.71980999681494, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-10-05T00:00:00\", \"total\": 118.64848126181764, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-10-12T00:00:00\", \"total\": 115.14824831014471, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-10-19T00:00:00\", \"total\": 115.14824831014471, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-10-26T00:00:00\", \"total\": 118.3627004184825, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-11-02T00:00:00\", \"total\": 118.00559084015006, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-11-09T00:00:00\", \"total\": 118.2913716834852, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-11-16T00:00:00\", \"total\": 125.36316632182834, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-11-23T00:00:00\", \"total\": 139.2205088518337, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-11-30T00:00:00\", \"total\": 131.57761843016615, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-12-07T00:00:00\", \"total\": 132.29183758683104, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-12-14T00:00:00\", \"total\": 125.07738547849323, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-12-21T00:00:00\", \"total\": 131.14871421349056, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1924-12-28T00:00:00\", \"total\": 117.57715252682031, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-01-04T00:00:00\", \"total\": 116.3627004184825, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-01-11T00:00:00\", \"total\": 122.00559084015006, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-01-18T00:00:00\", \"total\": 117.22004294848787, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-01-25T00:00:00\", \"total\": 121.79160463515811, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-02-01T00:00:00\", \"total\": 133.57761843016615, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-02-08T00:00:00\", \"total\": 136.4349609601715, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-02-15T00:00:00\", \"total\": 129.4349609601715, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-02-22T00:00:00\", \"total\": 138.07785138183908, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-03-01T00:00:00\", \"total\": 131.2205088518337, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-03-08T00:00:00\", \"total\": 148.4349609601715, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-03-15T00:00:00\", \"total\": 134.36363222517417, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-03-22T00:00:00\", \"total\": 139.00652264684174, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-03-29T00:00:00\", \"total\": 140.64941306850932, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-04-05T00:00:00\", \"total\": 141.8638651768471, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-04-12T00:00:00\", \"total\": 139.22097475517955, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-04-19T00:00:00\", \"total\": 136.14964602018222, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-04-26T00:00:00\", \"total\": 142.72120770685245, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-05-03T00:00:00\", \"total\": 144.36409812852003, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-05-10T00:00:00\", \"total\": 139.50675559851467, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-05-17T00:00:00\", \"total\": 145.72074180350663, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-05-24T00:00:00\", \"total\": 134.29230349017686, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-05-31T00:00:00\", \"total\": 134.9351939118444, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-06-07T00:00:00\", \"total\": 140.36363222517417, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-06-14T00:00:00\", \"total\": 129.64894716516346, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-06-21T00:00:00\", \"total\": 118.64848126181764, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-06-28T00:00:00\", \"total\": 110.36223451513668, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-07-05T00:00:00\", \"total\": 96.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-07-12T00:00:00\", \"total\": 101.50489198513131, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-07-19T00:00:00\", \"total\": 99.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-07-26T00:00:00\", \"total\": 102.43356325013399, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-08-02T00:00:00\", \"total\": 104.07645367180154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-08-09T00:00:00\", \"total\": 101.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-08-16T00:00:00\", \"total\": 97.86200156346375, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-08-23T00:00:00\", \"total\": 96.86200156346375, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-08-30T00:00:00\", \"total\": 100.86200156346375, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-09-06T00:00:00\", \"total\": 96.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-09-13T00:00:00\", \"total\": 100.50489198513131, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-09-20T00:00:00\", \"total\": 106.00512493680424, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-09-27T00:00:00\", \"total\": 117.93426210515275, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-10-04T00:00:00\", \"total\": 124.9347280084986, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-10-11T00:00:00\", \"total\": 131.1491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-10-18T00:00:00\", \"total\": 142.14964602018222, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-10-25T00:00:00\", \"total\": 147.43542686351734, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-11-01T00:00:00\", \"total\": 140.57808433351198, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-11-08T00:00:00\", \"total\": 144.5072215018605, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-11-15T00:00:00\", \"total\": 152.4358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-11-22T00:00:00\", \"total\": 166.86479698353878, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-11-29T00:00:00\", \"total\": 169.50768740520635, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-12-06T00:00:00\", \"total\": 177.50768740520635, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-12-13T00:00:00\", \"total\": 177.2937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-12-20T00:00:00\", \"total\": 166.65081077854683, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1925-12-27T00:00:00\", \"total\": 167.15104373021975, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-01-03T00:00:00\", \"total\": 168.93659162188195, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-01-10T00:00:00\", \"total\": 167.57948204354952, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-01-17T00:00:00\", \"total\": 165.0792490918766, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-01-24T00:00:00\", \"total\": 170.0792490918766, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-01-31T00:00:00\", \"total\": 165.43635867020902, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-02-07T00:00:00\", \"total\": 170.0792490918766, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-02-14T00:00:00\", \"total\": 165.86479698353878, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-02-21T00:00:00\", \"total\": 150.07878318853074, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-02-28T00:00:00\", \"total\": 165.7216736101983, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-03-07T00:00:00\", \"total\": 167.5072215018605, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-03-14T00:00:00\", \"total\": 147.0069885501876, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-03-21T00:00:00\", \"total\": 166.0069885501876, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-03-28T00:00:00\", \"total\": 156.9351939118444, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-04-04T00:00:00\", \"total\": 155.50675559851467, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-04-11T00:00:00\", \"total\": 147.79207053850394, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-04-18T00:00:00\", \"total\": 156.29183758683104, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-04-25T00:00:00\", \"total\": 144.0060567434959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-05-02T00:00:00\", \"total\": 152.2205088518337, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-05-09T00:00:00\", \"total\": 140.2205088518337, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-05-16T00:00:00\", \"total\": 139.72074180350663, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-05-23T00:00:00\", \"total\": 144.8638651768471, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-05-30T00:00:00\", \"total\": 129.4349609601715, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-06-06T00:00:00\", \"total\": 135.29230349017686, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-06-13T00:00:00\", \"total\": 134.1491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-06-20T00:00:00\", \"total\": 116.43449505682567, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-06-27T00:00:00\", \"total\": 123.79160463515811, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-07-04T00:00:00\", \"total\": 125.22004294848787, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-07-11T00:00:00\", \"total\": 124.14871421349055, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-07-18T00:00:00\", \"total\": 118.86293337015543, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-07-25T00:00:00\", \"total\": 125.14871421349055, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-08-01T00:00:00\", \"total\": 120.00559084015006, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-08-08T00:00:00\", \"total\": 123.93426210515275, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-08-15T00:00:00\", \"total\": 121.79160463515811, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-08-22T00:00:00\", \"total\": 127.14871421349055, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-08-29T00:00:00\", \"total\": 114.64848126181764, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-09-05T00:00:00\", \"total\": 108.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-09-12T00:00:00\", \"total\": 102.21911114179619, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-09-19T00:00:00\", \"total\": 118.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-09-26T00:00:00\", \"total\": 127.2913716834852, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-10-03T00:00:00\", \"total\": 120.86293337015543, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-10-10T00:00:00\", \"total\": 126.43449505682567, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-10-17T00:00:00\", \"total\": 136.0060567434959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-10-24T00:00:00\", \"total\": 122.07691957514739, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-10-31T00:00:00\", \"total\": 120.36223451513668, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-11-07T00:00:00\", \"total\": 116.21957704514203, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-11-14T00:00:00\", \"total\": 133.64848126181764, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-11-21T00:00:00\", \"total\": 135.57715252682033, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-11-28T00:00:00\", \"total\": 133.86293337015542, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-12-05T00:00:00\", \"total\": 152.22004294848787, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-12-12T00:00:00\", \"total\": 115.3627004184825, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-12-19T00:00:00\", \"total\": 123.43402915347983, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1926-12-26T00:00:00\", \"total\": 112.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-01-02T00:00:00\", \"total\": 107.21957704514203, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-01-09T00:00:00\", \"total\": 113.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-01-16T00:00:00\", \"total\": 111.14778240679887, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-01-23T00:00:00\", \"total\": 112.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-01-30T00:00:00\", \"total\": 109.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-02-06T00:00:00\", \"total\": 106.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-02-13T00:00:00\", \"total\": 113.71934409346912, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-02-20T00:00:00\", \"total\": 110.6480153584718, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-02-27T00:00:00\", \"total\": 111.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-03-06T00:00:00\", \"total\": 113.93379620180691, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-03-13T00:00:00\", \"total\": 119.2913716834852, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-03-20T00:00:00\", \"total\": 125.50582379182299, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-03-27T00:00:00\", \"total\": 121.14871421349055, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-04-03T00:00:00\", \"total\": 121.86293337015543, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-04-10T00:00:00\", \"total\": 118.43402915347983, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-04-17T00:00:00\", \"total\": 114.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-04-24T00:00:00\", \"total\": 101.21911114179619, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-05-01T00:00:00\", \"total\": 111.79067282846643, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-05-08T00:00:00\", \"total\": 110.36223451513668, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-05-15T00:00:00\", \"total\": 111.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-05-22T00:00:00\", \"total\": 104.64754945512595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-05-29T00:00:00\", \"total\": 99.07598776845572, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-06-05T00:00:00\", \"total\": 113.00512493680424, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-06-12T00:00:00\", \"total\": 102.43356325013399, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-06-19T00:00:00\", \"total\": 106.07645367180154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-06-26T00:00:00\", \"total\": 102.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-07-03T00:00:00\", \"total\": 97.71887819012328, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-07-10T00:00:00\", \"total\": 99.86200156346375, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-07-17T00:00:00\", \"total\": 97.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-07-24T00:00:00\", \"total\": 98.36176861179084, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-07-31T00:00:00\", \"total\": 91.50442608178547, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-08-07T00:00:00\", \"total\": 99.93286439511523, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-08-14T00:00:00\", \"total\": 85.21817933510451, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-08-21T00:00:00\", \"total\": 84.21817933510451, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-08-28T00:00:00\", \"total\": 81.57528891343695, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-09-04T00:00:00\", \"total\": 82.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-09-11T00:00:00\", \"total\": 76.5034942750938, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-09-18T00:00:00\", \"total\": 82.07505596176404, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-09-25T00:00:00\", \"total\": 84.93239849176939, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-10-02T00:00:00\", \"total\": 84.93239849176939, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-10-09T00:00:00\", \"total\": 93.07552186510988, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-10-16T00:00:00\", \"total\": 92.43309734678814, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-10-23T00:00:00\", \"total\": 97.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-10-30T00:00:00\", \"total\": 98.57575481678279, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-11-06T00:00:00\", \"total\": 88.07552186510988, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-11-13T00:00:00\", \"total\": 93.28997397344767, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-11-20T00:00:00\", \"total\": 95.93286439511523, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-11-27T00:00:00\", \"total\": 103.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-12-04T00:00:00\", \"total\": 115.36176861179084, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-12-11T00:00:00\", \"total\": 112.71887819012328, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-12-18T00:00:00\", \"total\": 98.79020692512059, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1927-12-25T00:00:00\", \"total\": 100.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-01-01T00:00:00\", \"total\": 100.64754945512595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-01-08T00:00:00\", \"total\": 99.07598776845572, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-01-15T00:00:00\", \"total\": 91.07552186510988, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-01-22T00:00:00\", \"total\": 83.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-01-29T00:00:00\", \"total\": 89.43216554009648, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-02-05T00:00:00\", \"total\": 76.21724752841283, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-02-12T00:00:00\", \"total\": 73.93146668507771, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-02-19T00:00:00\", \"total\": 82.28857626341016, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-02-26T00:00:00\", \"total\": 70.71701457673991, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-03-04T00:00:00\", \"total\": 57.788343311737236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-03-11T00:00:00\", \"total\": 62.431233733404795, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-03-18T00:00:00\", \"total\": 60.00279542007503, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-03-25T00:00:00\", \"total\": 64.14591879341552, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-04-01T00:00:00\", \"total\": 71.64615174508843, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-04-08T00:00:00\", \"total\": 88.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-04-15T00:00:00\", \"total\": 80.93239849176939, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-04-22T00:00:00\", \"total\": 89.78974102177476, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-04-29T00:00:00\", \"total\": 90.93286439511523, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-05-06T00:00:00\", \"total\": 92.71887819012328, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-05-13T00:00:00\", \"total\": 90.07598776845572, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-05-20T00:00:00\", \"total\": 101.0046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-05-27T00:00:00\", \"total\": 94.43309734678814, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-06-03T00:00:00\", \"total\": 91.21864523845035, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-06-10T00:00:00\", \"total\": 95.07552186510988, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-06-17T00:00:00\", \"total\": 89.50396017843963, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-06-24T00:00:00\", \"total\": 88.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-07-01T00:00:00\", \"total\": 90.93239849176939, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-07-08T00:00:00\", \"total\": 84.64661764843427, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-07-15T00:00:00\", \"total\": 79.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-07-22T00:00:00\", \"total\": 77.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-07-29T00:00:00\", \"total\": 78.07505596176404, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-08-05T00:00:00\", \"total\": 72.7179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-08-12T00:00:00\", \"total\": 79.7179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-08-19T00:00:00\", \"total\": 83.43216554009648, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-08-26T00:00:00\", \"total\": 73.5034942750938, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-09-02T00:00:00\", \"total\": 69.21771343175867, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-09-09T00:00:00\", \"total\": 76.7179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-09-16T00:00:00\", \"total\": 88.50396017843963, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-09-23T00:00:00\", \"total\": 86.07552186510988, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-09-30T00:00:00\", \"total\": 91.64708355178011, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-10-07T00:00:00\", \"total\": 99.57575481678279, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-10-14T00:00:00\", \"total\": 94.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-10-21T00:00:00\", \"total\": 101.64754945512595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-10-28T00:00:00\", \"total\": 99.0046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-11-04T00:00:00\", \"total\": 112.07645367180154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-11-11T00:00:00\", \"total\": 116.6480153584718, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-11-18T00:00:00\", \"total\": 113.36223451513668, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-11-25T00:00:00\", \"total\": 116.14824831014471, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-12-02T00:00:00\", \"total\": 126.79113873181227, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-12-09T00:00:00\", \"total\": 119.50535788847715, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-12-16T00:00:00\", \"total\": 121.14824831014471, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-12-23T00:00:00\", \"total\": 112.36223451513668, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1928-12-30T00:00:00\", \"total\": 117.00512493680424, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-01-06T00:00:00\", \"total\": 113.29090578013935, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-01-13T00:00:00\", \"total\": 123.86246746680959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-01-20T00:00:00\", \"total\": 128.00559084015006, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-01-27T00:00:00\", \"total\": 134.43449505682565, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-02-03T00:00:00\", \"total\": 132.0060567434959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-02-10T00:00:00\", \"total\": 135.36316632182834, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-02-17T00:00:00\", \"total\": 144.07738547849323, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-02-24T00:00:00\", \"total\": 127.43449505682567, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-03-03T00:00:00\", \"total\": 122.57715252682031, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-03-10T00:00:00\", \"total\": 129.14871421349056, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-03-17T00:00:00\", \"total\": 136.29183758683104, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-03-24T00:00:00\", \"total\": 143.64894716516346, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-03-31T00:00:00\", \"total\": 132.9347280084986, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-04-07T00:00:00\", \"total\": 135.86339927350127, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-04-14T00:00:00\", \"total\": 142.2205088518337, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-04-21T00:00:00\", \"total\": 135.36316632182834, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-04-28T00:00:00\", \"total\": 146.79207053850394, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-05-05T00:00:00\", \"total\": 148.00652264684174, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-05-12T00:00:00\", \"total\": 128.7202759001608, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-05-19T00:00:00\", \"total\": 131.43449505682565, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-05-26T00:00:00\", \"total\": 118.71980999681494, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-06-02T00:00:00\", \"total\": 118.21957704514203, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-06-09T00:00:00\", \"total\": 124.43356325013399, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-06-16T00:00:00\", \"total\": 108.43356325013399, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-06-23T00:00:00\", \"total\": 106.21911114179619, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-06-30T00:00:00\", \"total\": 99.64754945512595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-07-07T00:00:00\", \"total\": 108.86200156346375, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-07-14T00:00:00\", \"total\": 91.28997397344767, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-07-21T00:00:00\", \"total\": 82.1468506001072, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-07-28T00:00:00\", \"total\": 80.28950807010183, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-08-04T00:00:00\", \"total\": 93.86106975677207, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-08-11T00:00:00\", \"total\": 87.57528891343695, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-08-18T00:00:00\", \"total\": 84.7179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-08-25T00:00:00\", \"total\": 82.57528891343695, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-09-01T00:00:00\", \"total\": 75.64661764843427, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-09-08T00:00:00\", \"total\": 75.7179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-09-15T00:00:00\", \"total\": 78.36083680509915, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-09-22T00:00:00\", \"total\": 76.7179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-09-29T00:00:00\", \"total\": 91.21817933510451, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-10-06T00:00:00\", \"total\": 92.28997397344767, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-10-13T00:00:00\", \"total\": 97.57575481678279, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-10-20T00:00:00\", \"total\": 105.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-10-27T00:00:00\", \"total\": 105.0046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-11-03T00:00:00\", \"total\": 96.71887819012328, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-11-10T00:00:00\", \"total\": 108.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-11-17T00:00:00\", \"total\": 107.21911114179619, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-11-24T00:00:00\", \"total\": 98.93333029846107, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-12-01T00:00:00\", \"total\": 103.29043987679351, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-12-08T00:00:00\", \"total\": 107.14778240679887, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-12-15T00:00:00\", \"total\": 100.57622072012863, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-12-22T00:00:00\", \"total\": 97.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1929-12-29T00:00:00\", \"total\": 100.71887819012328, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-01-05T00:00:00\", \"total\": 94.14731650345303, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-01-12T00:00:00\", \"total\": 81.36083680509915, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-01-19T00:00:00\", \"total\": 84.14638469676136, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-01-26T00:00:00\", \"total\": 75.78880921508308, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-02-02T00:00:00\", \"total\": 66.35990499840747, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-02-09T00:00:00\", \"total\": 60.50256246840212, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-02-16T00:00:00\", \"total\": 58.21678162506699, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-02-23T00:00:00\", \"total\": 54.07365825172651, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-03-02T00:00:00\", \"total\": 51.85920614338872, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-03-09T00:00:00\", \"total\": 47.43030192671311, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-03-16T00:00:00\", \"total\": 46.85874024004288, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-03-23T00:00:00\", \"total\": 42.85874024004288, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-03-30T00:00:00\", \"total\": 41.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-04-06T00:00:00\", \"total\": 39.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-04-13T00:00:00\", \"total\": 33.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-04-20T00:00:00\", \"total\": 26.00093180669168, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-04-27T00:00:00\", \"total\": 26.715150963356557, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-05-04T00:00:00\", \"total\": 23.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-05-11T00:00:00\", \"total\": 22.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-05-18T00:00:00\", \"total\": 14.357575481678278, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-05-25T00:00:00\", \"total\": 15.357575481678278, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-06-01T00:00:00\", \"total\": 18.35757548167828, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-06-08T00:00:00\", \"total\": 15.572027590016079, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-06-15T00:00:00\", \"total\": 19.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-06-22T00:00:00\", \"total\": 24.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-06-29T00:00:00\", \"total\": 29.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-07-06T00:00:00\", \"total\": 28.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-07-13T00:00:00\", \"total\": 32.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-07-20T00:00:00\", \"total\": 23.21491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-07-27T00:00:00\", \"total\": 23.14358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-08-03T00:00:00\", \"total\": 38.71515096335656, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-08-10T00:00:00\", \"total\": 30.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-08-17T00:00:00\", \"total\": 19.8578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-08-24T00:00:00\", \"total\": 16.71468506001072, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-08-31T00:00:00\", \"total\": 20.71468506001072, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-09-07T00:00:00\", \"total\": 15.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-09-14T00:00:00\", \"total\": 9.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-09-21T00:00:00\", \"total\": 14.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-09-28T00:00:00\", \"total\": 15.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-10-05T00:00:00\", \"total\": 14.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-10-12T00:00:00\", \"total\": 18.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-10-19T00:00:00\", \"total\": 19.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-10-26T00:00:00\", \"total\": 14.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-11-02T00:00:00\", \"total\": 16.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-11-09T00:00:00\", \"total\": 17.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-11-16T00:00:00\", \"total\": 16.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-11-23T00:00:00\", \"total\": 16.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-11-30T00:00:00\", \"total\": 16.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-12-07T00:00:00\", \"total\": 20.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-12-14T00:00:00\", \"total\": 21.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-12-21T00:00:00\", \"total\": 17.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1930-12-28T00:00:00\", \"total\": 18.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-01-04T00:00:00\", \"total\": 20.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-01-11T00:00:00\", \"total\": 19.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-01-18T00:00:00\", \"total\": 19.572027590016077, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-01-25T00:00:00\", \"total\": 26.572027590016077, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-02-01T00:00:00\", \"total\": 18.572027590016077, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-02-08T00:00:00\", \"total\": 20.572027590016077, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-02-15T00:00:00\", \"total\": 22.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-02-22T00:00:00\", \"total\": 25.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-03-01T00:00:00\", \"total\": 29.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-03-08T00:00:00\", \"total\": 23.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-03-15T00:00:00\", \"total\": 21.8578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-03-22T00:00:00\", \"total\": 22.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-03-29T00:00:00\", \"total\": 18.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-04-05T00:00:00\", \"total\": 14.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-04-12T00:00:00\", \"total\": 18.35757548167828, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-04-19T00:00:00\", \"total\": 15.786013795008039, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-04-26T00:00:00\", \"total\": 14.786013795008039, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-05-03T00:00:00\", \"total\": 19.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-05-10T00:00:00\", \"total\": 20.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-05-17T00:00:00\", \"total\": 13.786013795008039, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-05-24T00:00:00\", \"total\": 14.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-05-31T00:00:00\", \"total\": 13.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-06-07T00:00:00\", \"total\": 13.214452108337799, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-06-14T00:00:00\", \"total\": 11.214452108337799, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-06-21T00:00:00\", \"total\": 17.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-06-28T00:00:00\", \"total\": 9.214452108337799, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-07-05T00:00:00\", \"total\": 10.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-07-12T00:00:00\", \"total\": 18.64289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-07-19T00:00:00\", \"total\": 10.642890421667559, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-07-26T00:00:00\", \"total\": 11.642890421667559, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-08-02T00:00:00\", \"total\": 5.64289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-08-09T00:00:00\", \"total\": 18.64289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-08-16T00:00:00\", \"total\": 4.64289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-08-23T00:00:00\", \"total\": 16.64289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-08-30T00:00:00\", \"total\": 7.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-09-06T00:00:00\", \"total\": 10.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-09-13T00:00:00\", \"total\": 13.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-09-20T00:00:00\", \"total\": 12.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-09-27T00:00:00\", \"total\": 8.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-10-04T00:00:00\", \"total\": 12.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-10-11T00:00:00\", \"total\": 10.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-10-18T00:00:00\", \"total\": 14.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-10-25T00:00:00\", \"total\": 17.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-11-01T00:00:00\", \"total\": 13.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-11-08T00:00:00\", \"total\": 9.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-11-15T00:00:00\", \"total\": 7.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-11-22T00:00:00\", \"total\": 14.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-11-29T00:00:00\", \"total\": 24.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-12-06T00:00:00\", \"total\": 15.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-12-13T00:00:00\", \"total\": 20.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-12-20T00:00:00\", \"total\": 15.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1931-12-27T00:00:00\", \"total\": 16.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-01-03T00:00:00\", \"total\": 16.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-01-10T00:00:00\", \"total\": 16.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-01-17T00:00:00\", \"total\": 17.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-01-24T00:00:00\", \"total\": 17.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-01-31T00:00:00\", \"total\": 14.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-02-07T00:00:00\", \"total\": 19.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-02-14T00:00:00\", \"total\": 18.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-02-21T00:00:00\", \"total\": 16.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-02-28T00:00:00\", \"total\": 19.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-03-06T00:00:00\", \"total\": 12.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-03-13T00:00:00\", \"total\": 11.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-03-20T00:00:00\", \"total\": 25.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-03-27T00:00:00\", \"total\": 18.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-04-03T00:00:00\", \"total\": 18.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-04-10T00:00:00\", \"total\": 19.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-04-17T00:00:00\", \"total\": 15.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-04-24T00:00:00\", \"total\": 11.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-05-01T00:00:00\", \"total\": 12.642890421667559, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-05-08T00:00:00\", \"total\": 9.642890421667559, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-05-15T00:00:00\", \"total\": 12.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-05-22T00:00:00\", \"total\": 10.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-05-29T00:00:00\", \"total\": 11.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-06-05T00:00:00\", \"total\": 13.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-06-12T00:00:00\", \"total\": 10.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-06-19T00:00:00\", \"total\": 5.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-06-26T00:00:00\", \"total\": 10.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-07-03T00:00:00\", \"total\": 14.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-07-10T00:00:00\", \"total\": 12.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-07-17T00:00:00\", \"total\": 9.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-07-24T00:00:00\", \"total\": 9.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-07-31T00:00:00\", \"total\": 4.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-08-07T00:00:00\", \"total\": 16.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-08-14T00:00:00\", \"total\": 7.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-08-21T00:00:00\", \"total\": 6.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-08-28T00:00:00\", \"total\": 12.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-09-04T00:00:00\", \"total\": 10.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-09-11T00:00:00\", \"total\": 10.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-09-18T00:00:00\", \"total\": 10.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-09-25T00:00:00\", \"total\": 10.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-10-02T00:00:00\", \"total\": 12.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-10-09T00:00:00\", \"total\": 19.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-10-16T00:00:00\", \"total\": 21.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-10-23T00:00:00\", \"total\": 23.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-10-30T00:00:00\", \"total\": 25.00093180669168, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-11-06T00:00:00\", \"total\": 23.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-11-13T00:00:00\", \"total\": 26.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-11-20T00:00:00\", \"total\": 41.501164758364595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-11-27T00:00:00\", \"total\": 37.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-12-04T00:00:00\", \"total\": 35.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-12-11T00:00:00\", \"total\": 37.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-12-18T00:00:00\", \"total\": 30.715150963356557, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1932-12-25T00:00:00\", \"total\": 17.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-01-01T00:00:00\", \"total\": 21.428904216675598, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-01-08T00:00:00\", \"total\": 21.14358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-01-15T00:00:00\", \"total\": 43.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-01-22T00:00:00\", \"total\": 51.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-01-29T00:00:00\", \"total\": 53.144521083377995, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-02-05T00:00:00\", \"total\": 48.3589731917158, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-02-12T00:00:00\", \"total\": 53.644754035050916, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-02-19T00:00:00\", \"total\": 47.644754035050916, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-02-26T00:00:00\", \"total\": 45.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-03-05T00:00:00\", \"total\": 45.07319234838068, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-03-12T00:00:00\", \"total\": 49.00186361338336, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-03-19T00:00:00\", \"total\": 44.644754035050916, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-03-26T00:00:00\", \"total\": 44.00186361338336, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-04-02T00:00:00\", \"total\": 44.43030192671311, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-04-09T00:00:00\", \"total\": 42.501630661710436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-04-16T00:00:00\", \"total\": 34.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-04-23T00:00:00\", \"total\": 35.85874024004288, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-04-30T00:00:00\", \"total\": 44.787411505045554, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-05-07T00:00:00\", \"total\": 47.287644456718475, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-05-14T00:00:00\", \"total\": 54.430767830058954, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-05-21T00:00:00\", \"total\": 60.931000781731875, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-05-28T00:00:00\", \"total\": 57.288110360064316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-06-04T00:00:00\", \"total\": 60.64521993839676, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-06-11T00:00:00\", \"total\": 51.21631572172116, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-06-18T00:00:00\", \"total\": 48.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-06-25T00:00:00\", \"total\": 38.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-07-02T00:00:00\", \"total\": 31.001397710037516, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-07-09T00:00:00\", \"total\": 38.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-07-16T00:00:00\", \"total\": 30.358507288369957, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-07-23T00:00:00\", \"total\": 28.786945601699717, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-07-30T00:00:00\", \"total\": 29.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-08-06T00:00:00\", \"total\": 16.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-08-13T00:00:00\", \"total\": 23.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-08-20T00:00:00\", \"total\": 17.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-08-27T00:00:00\", \"total\": 26.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-09-03T00:00:00\", \"total\": 24.2867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-09-10T00:00:00\", \"total\": 25.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-09-17T00:00:00\", \"total\": 31.643822228359237, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-09-24T00:00:00\", \"total\": 30.572493493361918, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-10-01T00:00:00\", \"total\": 30.144055180032158, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-10-08T00:00:00\", \"total\": 35.501164758364595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-10-15T00:00:00\", \"total\": 55.7156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-10-22T00:00:00\", \"total\": 48.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-10-29T00:00:00\", \"total\": 50.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-11-05T00:00:00\", \"total\": 60.57295939670776, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-11-12T00:00:00\", \"total\": 63.787411505045554, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-11-19T00:00:00\", \"total\": 64.28764445671848, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-11-26T00:00:00\", \"total\": 63.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-12-03T00:00:00\", \"total\": 61.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-12-10T00:00:00\", \"total\": 73.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-12-17T00:00:00\", \"total\": 71.7878774083914, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-12-24T00:00:00\", \"total\": 73.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1933-12-31T00:00:00\", \"total\": 74.43076783005895, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-01-07T00:00:00\", \"total\": 63.85920614338872, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-01-14T00:00:00\", \"total\": 71.5734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-01-21T00:00:00\", \"total\": 69.93053487838603, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-01-28T00:00:00\", \"total\": 74.07319234838067, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-02-04T00:00:00\", \"total\": 65.78741150504555, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-02-11T00:00:00\", \"total\": 60.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-02-18T00:00:00\", \"total\": 55.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-02-25T00:00:00\", \"total\": 54.07272644503484, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-03-04T00:00:00\", \"total\": 60.7156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-03-11T00:00:00\", \"total\": 56.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-03-18T00:00:00\", \"total\": 49.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-03-25T00:00:00\", \"total\": 38.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-04-01T00:00:00\", \"total\": 39.57249349336192, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-04-08T00:00:00\", \"total\": 46.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-04-15T00:00:00\", \"total\": 49.2867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-04-22T00:00:00\", \"total\": 51.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-04-29T00:00:00\", \"total\": 35.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-05-06T00:00:00\", \"total\": 44.64382222835924, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-05-13T00:00:00\", \"total\": 40.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-05-20T00:00:00\", \"total\": 41.072260541689, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-05-27T00:00:00\", \"total\": 34.500698855018754, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-06-03T00:00:00\", \"total\": 40.500698855018754, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-06-10T00:00:00\", \"total\": 37.14358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-06-17T00:00:00\", \"total\": 28.14358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-06-24T00:00:00\", \"total\": 35.072260541689, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-07-01T00:00:00\", \"total\": 27.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-07-08T00:00:00\", \"total\": 31.643822228359237, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-07-15T00:00:00\", \"total\": 53.64382222835924, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-07-22T00:00:00\", \"total\": 26.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-07-29T00:00:00\", \"total\": 33.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-08-05T00:00:00\", \"total\": 30.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-08-12T00:00:00\", \"total\": 24.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-08-19T00:00:00\", \"total\": 18.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-08-26T00:00:00\", \"total\": 24.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-09-02T00:00:00\", \"total\": 31.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-09-09T00:00:00\", \"total\": 28.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-09-16T00:00:00\", \"total\": 25.21491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-09-23T00:00:00\", \"total\": 27.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-09-30T00:00:00\", \"total\": 27.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-10-07T00:00:00\", \"total\": 24.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-10-14T00:00:00\", \"total\": 25.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-10-21T00:00:00\", \"total\": 13.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-10-28T00:00:00\", \"total\": 26.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-11-04T00:00:00\", \"total\": 29.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-11-11T00:00:00\", \"total\": 32.35757548167828, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-11-18T00:00:00\", \"total\": 28.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-11-25T00:00:00\", \"total\": 26.71468506001072, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-12-02T00:00:00\", \"total\": 33.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-12-09T00:00:00\", \"total\": 28.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-12-16T00:00:00\", \"total\": 27.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-12-23T00:00:00\", \"total\": 32.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1934-12-30T00:00:00\", \"total\": 30.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-01-06T00:00:00\", \"total\": 22.428904216675598, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-01-13T00:00:00\", \"total\": 32.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-01-20T00:00:00\", \"total\": 29.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-01-27T00:00:00\", \"total\": 43.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-02-03T00:00:00\", \"total\": 61.857808433351195, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-02-10T00:00:00\", \"total\": 53.14358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-02-17T00:00:00\", \"total\": 43.71515096335656, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-02-24T00:00:00\", \"total\": 41.71515096335656, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-03-03T00:00:00\", \"total\": 34.857808433351195, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-03-10T00:00:00\", \"total\": 41.14358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-03-17T00:00:00\", \"total\": 29.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-03-24T00:00:00\", \"total\": 40.500698855018754, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-03-31T00:00:00\", \"total\": 37.072260541689, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-04-07T00:00:00\", \"total\": 40.64382222835924, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-04-14T00:00:00\", \"total\": 52.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-04-21T00:00:00\", \"total\": 48.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-04-28T00:00:00\", \"total\": 44.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-05-05T00:00:00\", \"total\": 50.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-05-12T00:00:00\", \"total\": 45.57249349336192, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-05-19T00:00:00\", \"total\": 42.64382222835924, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-05-26T00:00:00\", \"total\": 47.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-06-02T00:00:00\", \"total\": 45.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-06-09T00:00:00\", \"total\": 40.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-06-16T00:00:00\", \"total\": 39.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-06-23T00:00:00\", \"total\": 41.501164758364595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-06-30T00:00:00\", \"total\": 38.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-07-07T00:00:00\", \"total\": 41.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-07-14T00:00:00\", \"total\": 38.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-07-21T00:00:00\", \"total\": 33.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-07-28T00:00:00\", \"total\": 37.57249349336192, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-08-04T00:00:00\", \"total\": 32.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-08-11T00:00:00\", \"total\": 31.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-08-18T00:00:00\", \"total\": 25.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-08-25T00:00:00\", \"total\": 33.072260541689, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-09-01T00:00:00\", \"total\": 27.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-09-08T00:00:00\", \"total\": 36.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-09-15T00:00:00\", \"total\": 37.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-09-22T00:00:00\", \"total\": 45.857808433351195, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-09-29T00:00:00\", \"total\": 34.214918011683636, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-10-06T00:00:00\", \"total\": 41.35757548167828, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-10-13T00:00:00\", \"total\": 42.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-10-20T00:00:00\", \"total\": 39.857808433351195, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-10-27T00:00:00\", \"total\": 56.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-11-03T00:00:00\", \"total\": 52.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-11-10T00:00:00\", \"total\": 64.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-11-17T00:00:00\", \"total\": 63.57295939670776, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-11-24T00:00:00\", \"total\": 68.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-12-01T00:00:00\", \"total\": 73.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-12-08T00:00:00\", \"total\": 61.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-12-15T00:00:00\", \"total\": 61.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-12-22T00:00:00\", \"total\": 52.001397710037516, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1935-12-29T00:00:00\", \"total\": 53.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-01-05T00:00:00\", \"total\": 50.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-01-12T00:00:00\", \"total\": 62.001397710037516, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-01-19T00:00:00\", \"total\": 63.144521083377995, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-01-26T00:00:00\", \"total\": 67.85874024004288, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-02-02T00:00:00\", \"total\": 77.28764445671848, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-02-09T00:00:00\", \"total\": 80.5734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-02-16T00:00:00\", \"total\": 76.85920614338872, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-02-23T00:00:00\", \"total\": 77.14498698672384, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-03-01T00:00:00\", \"total\": 79.43076783005895, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-03-08T00:00:00\", \"total\": 82.07365825172651, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-03-15T00:00:00\", \"total\": 83.00232951672919, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-03-22T00:00:00\", \"total\": 81.93100078173188, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-03-29T00:00:00\", \"total\": 87.93100078173188, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-04-05T00:00:00\", \"total\": 79.93100078173188, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-04-12T00:00:00\", \"total\": 72.00232951672919, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-04-19T00:00:00\", \"total\": 65.7878774083914, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-04-26T00:00:00\", \"total\": 81.00232951672919, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-05-03T00:00:00\", \"total\": 87.64521993839676, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-05-10T00:00:00\", \"total\": 79.85920614338872, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-05-17T00:00:00\", \"total\": 76.43076783005895, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-05-24T00:00:00\", \"total\": 65.93053487838603, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-05-31T00:00:00\", \"total\": 68.93053487838603, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-06-07T00:00:00\", \"total\": 68.28764445671848, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-06-14T00:00:00\", \"total\": 56.3589731917158, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-06-21T00:00:00\", \"total\": 52.501630661710436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-06-28T00:00:00\", \"total\": 48.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-07-05T00:00:00\", \"total\": 46.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-07-12T00:00:00\", \"total\": 55.7156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-07-19T00:00:00\", \"total\": 43.7156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-07-26T00:00:00\", \"total\": 47.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-08-02T00:00:00\", \"total\": 54.42983602336728, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-08-09T00:00:00\", \"total\": 69.71561686670239, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-08-16T00:00:00\", \"total\": 21.215383915029477, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-08-23T00:00:00\", \"total\": 20.572493493361918, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-08-30T00:00:00\", \"total\": 35.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-09-06T00:00:00\", \"total\": 26.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-09-13T00:00:00\", \"total\": 31.5011647583646, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-09-20T00:00:00\", \"total\": 32.501164758364595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-09-27T00:00:00\", \"total\": 41.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-10-04T00:00:00\", \"total\": 37.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-10-11T00:00:00\", \"total\": 37.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-10-18T00:00:00\", \"total\": 47.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-10-25T00:00:00\", \"total\": 39.7156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-11-01T00:00:00\", \"total\": 49.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-11-08T00:00:00\", \"total\": 46.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-11-15T00:00:00\", \"total\": 55.07272644503484, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-11-22T00:00:00\", \"total\": 61.85874024004288, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-11-29T00:00:00\", \"total\": 71.78741150504555, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-12-06T00:00:00\", \"total\": 77.71608277004825, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-12-13T00:00:00\", \"total\": 68.00186361338336, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-12-20T00:00:00\", \"total\": 61.787411505045554, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1936-12-27T00:00:00\", \"total\": 48.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-01-03T00:00:00\", \"total\": 61.787411505045554, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-01-10T00:00:00\", \"total\": 67.43030192671311, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-01-17T00:00:00\", \"total\": 56.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-01-24T00:00:00\", \"total\": 49.57295939670776, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-01-31T00:00:00\", \"total\": 63.7156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-02-07T00:00:00\", \"total\": 63.07272644503484, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-02-14T00:00:00\", \"total\": 55.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-02-21T00:00:00\", \"total\": 61.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-02-28T00:00:00\", \"total\": 52.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-03-07T00:00:00\", \"total\": 64.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-03-14T00:00:00\", \"total\": 45.501164758364595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-03-21T00:00:00\", \"total\": 56.07272644503484, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-03-28T00:00:00\", \"total\": 57.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-04-04T00:00:00\", \"total\": 43.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-04-11T00:00:00\", \"total\": 64.14405518003215, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-04-18T00:00:00\", \"total\": 55.501164758364595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-04-25T00:00:00\", \"total\": 59.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-05-02T00:00:00\", \"total\": 52.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-05-09T00:00:00\", \"total\": 57.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-05-16T00:00:00\", \"total\": 66.57249349336192, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-05-23T00:00:00\", \"total\": 60.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-05-30T00:00:00\", \"total\": 41.500698855018754, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-06-06T00:00:00\", \"total\": 30.21491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-06-13T00:00:00\", \"total\": 37.92913716834852, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-06-20T00:00:00\", \"total\": 39.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-06-27T00:00:00\", \"total\": 39.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-07-04T00:00:00\", \"total\": 45.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-07-11T00:00:00\", \"total\": 41.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-07-18T00:00:00\", \"total\": 29.71468506001072, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-07-25T00:00:00\", \"total\": 36.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-08-01T00:00:00\", \"total\": 40.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-08-08T00:00:00\", \"total\": 58.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-08-15T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-08-22T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-08-29T00:00:00\", \"total\": 5.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-09-05T00:00:00\", \"total\": 13.214452108337799, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-09-12T00:00:00\", \"total\": 29.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-09-19T00:00:00\", \"total\": 15.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-09-26T00:00:00\", \"total\": 30.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-10-03T00:00:00\", \"total\": 23.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-10-10T00:00:00\", \"total\": 38.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-10-17T00:00:00\", \"total\": 26.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-10-24T00:00:00\", \"total\": 25.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-10-31T00:00:00\", \"total\": 43.28578084333512, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-11-07T00:00:00\", \"total\": 31.285780843335118, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-11-14T00:00:00\", \"total\": 28.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-11-21T00:00:00\", \"total\": 46.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-11-28T00:00:00\", \"total\": 41.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-12-05T00:00:00\", \"total\": 37.571561686670236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-12-12T00:00:00\", \"total\": 60.571561686670236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-12-19T00:00:00\", \"total\": 47.571561686670236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1937-12-26T00:00:00\", \"total\": 36.571561686670236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-01-02T00:00:00\", \"total\": 21.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-01-09T00:00:00\", \"total\": 44.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-01-16T00:00:00\", \"total\": 52.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-01-23T00:00:00\", \"total\": 45.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-01-30T00:00:00\", \"total\": 47.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-02-06T00:00:00\", \"total\": 37.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-02-13T00:00:00\", \"total\": 61.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-02-20T00:00:00\", \"total\": 40.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-02-27T00:00:00\", \"total\": 53.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-03-06T00:00:00\", \"total\": 62.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-03-13T00:00:00\", \"total\": 48.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-03-20T00:00:00\", \"total\": 52.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-03-27T00:00:00\", \"total\": 37.07179463834316, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-04-03T00:00:00\", \"total\": 51.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-04-10T00:00:00\", \"total\": 52.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-04-17T00:00:00\", \"total\": 44.57202759001608, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-04-24T00:00:00\", \"total\": 40.92913716834852, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-05-01T00:00:00\", \"total\": 49.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-05-08T00:00:00\", \"total\": 53.71468506001072, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-05-15T00:00:00\", \"total\": 46.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-05-22T00:00:00\", \"total\": 46.78601379500804, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-05-29T00:00:00\", \"total\": 43.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-06-05T00:00:00\", \"total\": 49.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-06-12T00:00:00\", \"total\": 29.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-06-19T00:00:00\", \"total\": 23.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-06-26T00:00:00\", \"total\": 40.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-07-03T00:00:00\", \"total\": 43.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-07-10T00:00:00\", \"total\": 36.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-07-17T00:00:00\", \"total\": 44.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-07-24T00:00:00\", \"total\": 18.85734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-07-31T00:00:00\", \"total\": 75.85734253000535, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-08-07T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-08-14T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-08-21T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-08-28T00:00:00\", \"total\": 2.5715616866702398, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-09-04T00:00:00\", \"total\": 3.5715616866702398, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-09-11T00:00:00\", \"total\": 15.57156168667024, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-09-18T00:00:00\", \"total\": 16.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-09-25T00:00:00\", \"total\": 24.2144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-10-02T00:00:00\", \"total\": 15.214452108337799, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-10-09T00:00:00\", \"total\": 34.571561686670236, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-10-16T00:00:00\", \"total\": 38.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-10-23T00:00:00\", \"total\": 40.92867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-10-30T00:00:00\", \"total\": 44.4289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-11-06T00:00:00\", \"total\": 50.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-11-13T00:00:00\", \"total\": 53.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-11-20T00:00:00\", \"total\": 68.71561686670239, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-11-27T00:00:00\", \"total\": 62.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-12-04T00:00:00\", \"total\": 66.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-12-11T00:00:00\", \"total\": 57.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-12-18T00:00:00\", \"total\": 68.42983602336727, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1938-12-25T00:00:00\", \"total\": 68.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-01-01T00:00:00\", \"total\": 57.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-01-08T00:00:00\", \"total\": 61.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-01-15T00:00:00\", \"total\": 78.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-01-22T00:00:00\", \"total\": 53.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-01-29T00:00:00\", \"total\": 91.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-02-05T00:00:00\", \"total\": 68.42983602336727, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-02-12T00:00:00\", \"total\": 71.07272644503485, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-02-19T00:00:00\", \"total\": 80.42983602336727, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-02-26T00:00:00\", \"total\": 73.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-03-05T00:00:00\", \"total\": 78.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-03-12T00:00:00\", \"total\": 80.78741150504555, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-03-19T00:00:00\", \"total\": 83.144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-03-26T00:00:00\", \"total\": 72.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-04-02T00:00:00\", \"total\": 95.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-04-09T00:00:00\", \"total\": 90.144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-04-16T00:00:00\", \"total\": 63.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-04-23T00:00:00\", \"total\": 83.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-04-30T00:00:00\", \"total\": 81.42983602336727, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-05-07T00:00:00\", \"total\": 93.00139771003751, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-05-14T00:00:00\", \"total\": 67.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-05-21T00:00:00\", \"total\": 75.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-05-28T00:00:00\", \"total\": 71.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-06-04T00:00:00\", \"total\": 61.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-06-11T00:00:00\", \"total\": 58.57249349336192, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-06-18T00:00:00\", \"total\": 62.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-06-25T00:00:00\", \"total\": 62.001397710037516, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-07-02T00:00:00\", \"total\": 74.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-07-09T00:00:00\", \"total\": 73.64428813170508, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-07-16T00:00:00\", \"total\": 69.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-07-23T00:00:00\", \"total\": 101.07272644503485, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-07-30T00:00:00\", \"total\": 107.14405518003215, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-08-06T00:00:00\", \"total\": 20.572493493361918, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-08-13T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-08-20T00:00:00\", \"total\": 13.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-08-27T00:00:00\", \"total\": 12.21491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-09-03T00:00:00\", \"total\": 14.929137168348518, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-09-10T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-09-17T00:00:00\", \"total\": 26.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-09-24T00:00:00\", \"total\": 10.286246746680959, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-10-01T00:00:00\", \"total\": 9.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-10-08T00:00:00\", \"total\": 17.28624674668096, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-10-15T00:00:00\", \"total\": 22.6433563250134, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-10-22T00:00:00\", \"total\": 21.21491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-10-29T00:00:00\", \"total\": 37.500698855018754, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-11-05T00:00:00\", \"total\": 34.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-11-12T00:00:00\", \"total\": 47.072260541689, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-11-19T00:00:00\", \"total\": 46.072260541689, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-11-26T00:00:00\", \"total\": 40.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-12-03T00:00:00\", \"total\": 41.00093180669168, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-12-10T00:00:00\", \"total\": 42.2867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-12-17T00:00:00\", \"total\": 54.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-12-24T00:00:00\", \"total\": 47.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1939-12-31T00:00:00\", \"total\": 54.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-01-07T00:00:00\", \"total\": 43.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-01-14T00:00:00\", \"total\": 44.21538391502948, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-01-21T00:00:00\", \"total\": 71.42983602336727, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-01-28T00:00:00\", \"total\": 64.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-02-04T00:00:00\", \"total\": 61.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-02-11T00:00:00\", \"total\": 65.00139771003751, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-02-18T00:00:00\", \"total\": 69.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-02-25T00:00:00\", \"total\": 72.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-03-03T00:00:00\", \"total\": 82.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-03-10T00:00:00\", \"total\": 76.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-03-17T00:00:00\", \"total\": 89.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-03-24T00:00:00\", \"total\": 70.85874024004288, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-03-31T00:00:00\", \"total\": 60.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-04-07T00:00:00\", \"total\": 71.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-04-14T00:00:00\", \"total\": 56.001397710037516, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-04-21T00:00:00\", \"total\": 47.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-04-28T00:00:00\", \"total\": 58.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-05-05T00:00:00\", \"total\": 51.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-05-12T00:00:00\", \"total\": 48.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-05-19T00:00:00\", \"total\": 49.78694560169971, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-05-26T00:00:00\", \"total\": 38.501164758364595, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-06-02T00:00:00\", \"total\": 44.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-06-09T00:00:00\", \"total\": 37.144055180032154, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-06-16T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-06-23T00:00:00\", \"total\": 18.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-06-30T00:00:00\", \"total\": 22.14358927668632, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-07-07T00:00:00\", \"total\": 19.21491801168364, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-07-14T00:00:00\", \"total\": 19.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-07-21T00:00:00\", \"total\": 22.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-07-28T00:00:00\", \"total\": 23.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-08-04T00:00:00\", \"total\": 19.572027590016077, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-08-11T00:00:00\", \"total\": 17.929137168348518, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-08-18T00:00:00\", \"total\": 21.500698855018758, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-08-25T00:00:00\", \"total\": 22.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-09-01T00:00:00\", \"total\": 35.42937012002144, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-09-08T00:00:00\", \"total\": 25.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-09-15T00:00:00\", \"total\": 29.072260541688998, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-09-22T00:00:00\", \"total\": 30.78647969835388, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-09-29T00:00:00\", \"total\": 30.715150963356557, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-10-06T00:00:00\", \"total\": 35.00093180669168, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-10-13T00:00:00\", \"total\": 40.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-10-20T00:00:00\", \"total\": 44.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-10-27T00:00:00\", \"total\": 52.35850728836996, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-11-03T00:00:00\", \"total\": 46.07272644503484, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-11-10T00:00:00\", \"total\": 48.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-11-17T00:00:00\", \"total\": 45.28717855337264, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-11-24T00:00:00\", \"total\": 52.57295939670776, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-12-01T00:00:00\", \"total\": 50.21584981837532, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-12-08T00:00:00\", \"total\": 57.501630661710436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-12-15T00:00:00\", \"total\": 51.501630661710436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-12-22T00:00:00\", \"total\": 60.3589731917158, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1940-12-29T00:00:00\", \"total\": 46.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-01-05T00:00:00\", \"total\": 52.00186361338336, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-01-12T00:00:00\", \"total\": 54.644754035050916, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-01-19T00:00:00\", \"total\": 55.5734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-01-26T00:00:00\", \"total\": 52.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-02-02T00:00:00\", \"total\": 55.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-02-09T00:00:00\", \"total\": 57.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-02-16T00:00:00\", \"total\": 53.71608277004824, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-02-23T00:00:00\", \"total\": 53.930534878386034, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-03-02T00:00:00\", \"total\": 61.287644456718475, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-03-09T00:00:00\", \"total\": 58.144986986723836, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-03-16T00:00:00\", \"total\": 69.43076783005895, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-03-23T00:00:00\", \"total\": 63.144986986723836, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-03-30T00:00:00\", \"total\": 70.5734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-04-06T00:00:00\", \"total\": 62.07365825172651, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-04-13T00:00:00\", \"total\": 70.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-04-20T00:00:00\", \"total\": 67.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-04-27T00:00:00\", \"total\": 75.43076783005895, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-05-04T00:00:00\", \"total\": 72.07365825172651, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-05-11T00:00:00\", \"total\": 67.50209656505628, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-05-18T00:00:00\", \"total\": 63.144986986723836, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-05-25T00:00:00\", \"total\": 61.287644456718475, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-06-01T00:00:00\", \"total\": 77.85920614338872, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-06-08T00:00:00\", \"total\": 74.43076783005895, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-06-15T00:00:00\", \"total\": 68.14498698672384, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-06-22T00:00:00\", \"total\": 60.930534878386034, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-06-29T00:00:00\", \"total\": 58.287644456718475, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-07-06T00:00:00\", \"total\": 57.3589731917158, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-07-13T00:00:00\", \"total\": 64.93053487838603, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-07-20T00:00:00\", \"total\": 64.5734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-07-27T00:00:00\", \"total\": 66.28764445671848, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-08-03T00:00:00\", \"total\": 101.43030192671311, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-08-10T00:00:00\", \"total\": 38.85874024004288, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-08-17T00:00:00\", \"total\": 28.930068975040196, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-08-24T00:00:00\", \"total\": 25.144055180032158, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-08-31T00:00:00\", \"total\": 21.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-09-07T00:00:00\", \"total\": 38.64382222835924, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-09-14T00:00:00\", \"total\": 37.2867126500268, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-09-21T00:00:00\", \"total\": 46.858274336697036, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-09-28T00:00:00\", \"total\": 58.9300689750402, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-10-05T00:00:00\", \"total\": 68.144521083378, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-10-12T00:00:00\", \"total\": 63.43030192671311, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-10-19T00:00:00\", \"total\": 67.00232951672919, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-10-26T00:00:00\", \"total\": 86.78834331173724, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-11-02T00:00:00\", \"total\": 83.07412415507235, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-11-09T00:00:00\", \"total\": 89.21724752841283, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-11-16T00:00:00\", \"total\": 96.71748048008575, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-11-23T00:00:00\", \"total\": 88.0745900584182, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-11-30T00:00:00\", \"total\": 95.78880921508308, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-12-07T00:00:00\", \"total\": 91.14591879341552, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-12-14T00:00:00\", \"total\": 95.0745900584182, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-12-21T00:00:00\", \"total\": 55.28857626341016, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1941-12-28T00:00:00\", \"total\": 47.573891203399434, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-01-04T00:00:00\", \"total\": 38.5734253000536, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-01-11T00:00:00\", \"total\": 32.787411505045554, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-01-18T00:00:00\", \"total\": 26.358507288369957, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-01-25T00:00:00\", \"total\": 19.92960307169436, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-02-01T00:00:00\", \"total\": 17.35804138502412, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-02-08T00:00:00\", \"total\": 14.143589276686319, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-02-15T00:00:00\", \"total\": 10.929137168348518, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-02-22T00:00:00\", \"total\": 10.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-03-01T00:00:00\", \"total\": 9.00046590334584, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-03-08T00:00:00\", \"total\": 7.714685060010719, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-03-15T00:00:00\", \"total\": 5.7860137950080395, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-03-22T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-03-29T00:00:00\", \"total\": 10.14312337334048, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-04-05T00:00:00\", \"total\": 5.1431233733404795, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-04-12T00:00:00\", \"total\": 4.50023295167292, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-04-19T00:00:00\", \"total\": 3.8573425300053596, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-04-26T00:00:00\", \"total\": 1.2857808433351199, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-05-03T00:00:00\", \"total\": 3.64289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-05-10T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-05-17T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-05-24T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-05-31T00:00:00\", \"total\": 6.64289042166756, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-06-07T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-06-14T00:00:00\", \"total\": 0.6428904216675599, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-06-21T00:00:00\", \"total\": 6.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-06-28T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-07-05T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-07-12T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-07-19T00:00:00\", \"total\": 3.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-07-26T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-08-02T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-08-09T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-08-16T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-08-23T00:00:00\", \"total\": 2.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-08-30T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}, {\"date\": \"1942-09-06T00:00:00\", \"total\": 0.0, \"type\": \"Total estimated borrow events\"}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.Chart(...)"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# This chart visualizes the total subscription volumes, borrow event count, estimated borrow events, and total estimated borrow events over time via Altair.\n",
        "\n",
        "chart = alt.Chart(alt_borrowing_weekly).encode(\n",
        "    alt.X('date:T', axis=alt.Axis(title='Duration of the Lending Library')),\n",
        ").properties(\n",
        "    width=1200,\n",
        "    # height=chart_height\n",
        ")\n",
        "\n",
        "estimated_borrowing = chart.mark_line(strokeWidth=1).encode(\n",
        "    alt.Y(\"total\", axis=alt.Axis(title='Number of volumes')),\n",
        "    color=alt.Color(\"type\", title=\"\", scale=alt.Scale(domain=[\"Subscription volumes\", \"Borrow event count\", \"Estimated borrow events\", \"Total estimated borrow events\"],\n",
        "                                                      range=[\"#5276A7\", \"green\", \"lightgreen\", \"lightblue\"]))\n",
        ")\n",
        "estimated_borrowing\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "pp1YDPH-EkB0"
      },
      "outputs": [],
      "source": [
        "# figure 8\n",
        "# estimated_borrowing.save(\"fig8-estimated_borrowing.json\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "xSJY-HSGHdf2",
        "outputId": "a8b91d44-02bf-4dd9-9a89-05a96777cff8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = plt.gca()\n",
        "# total subscriptoin volumes\n",
        "borrowing_weekly.plot(kind='line',x='date',y='subscription_volumes',ax=ax, figsize=(25,7))\n",
        "# stacked area chart of actual borrows and estimated missing \n",
        "borrowing_weekly[['date', 'borrow_event_count', 'estimated_borrow_events']].plot.area(x='date', stacked=True, ax=ax)\n",
        "# line plot the total over the stack (not really helpful)\n",
        "# borrowing_weekly.plot(kind='line',x='date',y='total_estimated_borrow_events', color='yellow', ax=ax)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xkL6DQAHfFc",
        "outputId": "2269849d-23a6-486d-b306-27070032098d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Estimated total missing borrow events: 63330.49254804966\n",
            "Number of borrow events used to generate estimate: 17292\n"
          ]
        }
      ],
      "source": [
        "# Calculate the total number of estimated missing borrow events based on known subscriptions.\n",
        "# This can give us an idea of how many borrow events might be missing from our data.\n",
        "est_total_missing_borrows = borrowing_weekly.estimated_borrow_events.sum()\n",
        "print(f\"Estimated total missing borrow events: {est_total_missing_borrows}\")\n",
        "\n",
        "# Calculate the number of borrow events that were used to generate our estimate.\n",
        "# This can help us understand the size of the sample that our estimate is based on.\n",
        "total_borrows_used_to_estimate =  borrows_within_subscriptions.shape[0]\n",
        "print(f\"Number of borrow events used to generate estimate: {total_borrows_used_to_estimate}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "ELIXhYNCHiMH",
        "outputId": "653dbb32-65be-4e1e-d116-51613026c3c3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>item_year</th>\n",
              "      <th>item_notes</th>\n",
              "      <th>source_type</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1922</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Mr. Rhys</td>\n",
              "      <td>Rhys, Mr.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1902.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lending Library Card</td>\n",
              "      <td>Sylvia Beach, Rhys Lending Library Card, Box 4...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/67...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>rhys</td>\n",
              "      <td>conrad-typhoon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1923</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Eyre de Lanux / Mrs. Pierre de Lanux</td>\n",
              "      <td>de Lanux, Eyre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1919.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lending Library Card</td>\n",
              "      <td>Sylvia Beach, Eyre de Lanux Lending Library Ca...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/c5...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>lanux-eyre-de</td>\n",
              "      <td>woolf-night-day</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  event_type start_date    end_date  \\\n",
              "2     Borrow       1922  1922-08-23   \n",
              "8     Borrow       1923  1923-11-07   \n",
              "\n",
              "                                         member_uris  \\\n",
              "2  https://shakespeareandco.princeton.edu/members...   \n",
              "8  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "                           member_names member_sort_names  \\\n",
              "2                              Mr. Rhys         Rhys, Mr.   \n",
              "8  Eyre de Lanux / Mrs. Pierre de Lanux    de Lanux, Eyre   \n",
              "\n",
              "   subscription_price_paid  subscription_deposit subscription_duration  \\\n",
              "2                      NaN                   NaN                   NaN   \n",
              "8                      NaN                   NaN                   NaN   \n",
              "\n",
              "   subscription_duration_days  ...  item_year item_notes  \\\n",
              "2                         NaN  ...     1902.0        NaN   \n",
              "8                         NaN  ...     1919.0        NaN   \n",
              "\n",
              "            source_type                                    source_citation  \\\n",
              "2  Lending Library Card  Sylvia Beach, Rhys Lending Library Card, Box 4...   \n",
              "8  Lending Library Card  Sylvia Beach, Eyre de Lanux Lending Library Ca...   \n",
              "\n",
              "                                     source_manifest  \\\n",
              "2  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "8  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "\n",
              "                                        source_image  \\\n",
              "2  https://iiif.princeton.edu/loris/figgy_prod/67...   \n",
              "8  https://iiif.princeton.edu/loris/figgy_prod/c5...   \n",
              "\n",
              "                                    first_member_uri second_member_uri  \\\n",
              "2  https://shakespeareandco.princeton.edu/members...              None   \n",
              "8  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "       member_id          item_id  \n",
              "2           rhys   conrad-typhoon  \n",
              "8  lanux-eyre-de  woolf-night-day  \n",
              "\n",
              "[2 rows x 32 columns]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_borrows = events_df[events_df.event_type == 'Borrow']\n",
        "all_borrows.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-89QSl-IuNv"
      },
      "source": [
        "## Borrow estimates for specific periods\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWXSNHMxIwh-"
      },
      "source": [
        "### Feb 28 1931 — 75 books out\n",
        "\n",
        "Selected because of noted on the handwritten tally of the library books"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "rVI1u6nsI5Xv",
        "outputId": "e6fcb3b5-014d-410f-9af2-7f4595f852c3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>ratio</th>\n",
              "      <th>non_book_subs_vols</th>\n",
              "      <th>estimated_borrow_events</th>\n",
              "      <th>total_estimated_borrow_events</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>584</th>\n",
              "      <td>1931-02-01</td>\n",
              "      <td>16.0</td>\n",
              "      <td>7</td>\n",
              "      <td>16.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>18.0</td>\n",
              "      <td>11.572028</td>\n",
              "      <td>18.572028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>585</th>\n",
              "      <td>1931-02-08</td>\n",
              "      <td>16.0</td>\n",
              "      <td>9</td>\n",
              "      <td>16.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>18.0</td>\n",
              "      <td>11.572028</td>\n",
              "      <td>20.572028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>586</th>\n",
              "      <td>1931-02-15</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8</td>\n",
              "      <td>14.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>23.0</td>\n",
              "      <td>14.786480</td>\n",
              "      <td>22.786480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>587</th>\n",
              "      <td>1931-02-22</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11</td>\n",
              "      <td>14.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>23.0</td>\n",
              "      <td>14.786480</td>\n",
              "      <td>25.786480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          date  book_subscription_volumes  borrow_event_count  \\\n",
              "584 1931-02-01                       16.0                   7   \n",
              "585 1931-02-08                       16.0                   9   \n",
              "586 1931-02-15                       14.0                   8   \n",
              "587 1931-02-22                       14.0                  11   \n",
              "\n",
              "     card_subscription_volumes  subscription_volumes     ratio  \\\n",
              "584                       16.0                  34.0  0.437500   \n",
              "585                       16.0                  34.0  0.562500   \n",
              "586                       14.0                  37.0  0.571429   \n",
              "587                       14.0                  37.0  0.785714   \n",
              "\n",
              "     non_book_subs_vols  estimated_borrow_events  \\\n",
              "584                18.0                11.572028   \n",
              "585                18.0                11.572028   \n",
              "586                23.0                14.786480   \n",
              "587                23.0                14.786480   \n",
              "\n",
              "     total_estimated_borrow_events  \n",
              "584                      18.572028  \n",
              "585                      20.572028  \n",
              "586                      22.786480  \n",
              "587                      25.786480  "
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Filter the 'borrowing_weekly' DataFrame to include only rows where the date is in February 1931.\n",
        "filtered_borrowing_weekly = borrowing_weekly[(borrowing_weekly.date >= pd.to_datetime('1931-02-01')) & (borrowing_weekly.date <= pd.to_datetime('1931-02-28'))]\n",
        "filtered_borrowing_weekly\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AXiyn56JiOh"
      },
      "source": [
        "oh, but her tally is books out, not book events; can we compare that?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "usaYhVaUJ-zx",
        "outputId": "2cba4881-60b6-432f-8b19-88a58f5b81c4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>documented_borrows</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Claude Cahun / Mlle Lucie Schwob</td>\n",
              "      <td>Cahun, Claude</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 month</td>\n",
              "      <td>30.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Lucie Schwob Lending Library Car...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/3a...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>cahun</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1919-12-17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29915</th>\n",
              "      <td>Subscription</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Marcelle Flot</td>\n",
              "      <td>Flot, Marcelle</td>\n",
              "      <td>28.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 year</td>\n",
              "      <td>366.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>flot-marcelle</td>\n",
              "      <td>None</td>\n",
              "      <td>1919-11-17</td>\n",
              "      <td>1920-11-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         event_type  start_date    end_date  \\\n",
              "671    Subscription  1919-11-17  1919-12-17   \n",
              "29915  Subscription  1919-11-17  1920-11-17   \n",
              "\n",
              "                                             member_uris  \\\n",
              "671    https://shakespeareandco.princeton.edu/members...   \n",
              "29915  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "                           member_names member_sort_names  \\\n",
              "671    Claude Cahun / Mlle Lucie Schwob     Cahun, Claude   \n",
              "29915                     Marcelle Flot    Flot, Marcelle   \n",
              "\n",
              "       subscription_price_paid  subscription_deposit subscription_duration  \\\n",
              "671                        4.0                   NaN               1 month   \n",
              "29915                     28.0                   NaN                1 year   \n",
              "\n",
              "       subscription_duration_days  ...  \\\n",
              "671                          30.0  ...   \n",
              "29915                       366.0  ...   \n",
              "\n",
              "                                         source_citation  \\\n",
              "671    Sylvia Beach, Lucie Schwob Lending Library Car...   \n",
              "29915  Sylvia Beach, Logbooks 1919–1941, Sylvia Beach...   \n",
              "\n",
              "                                         source_manifest  \\\n",
              "671    https://figgy.princeton.edu/concern/scanned_re...   \n",
              "29915                                                NaN   \n",
              "\n",
              "                                            source_image  \\\n",
              "671    https://iiif.princeton.edu/loris/figgy_prod/3a...   \n",
              "29915                                                NaN   \n",
              "\n",
              "                                        first_member_uri second_member_uri  \\\n",
              "671    https://shakespeareandco.princeton.edu/members...              None   \n",
              "29915  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "           member_id  item_id start_datetime end_datetime documented_borrows  \n",
              "671            cahun     None     1919-11-17   1919-12-17                  3  \n",
              "29915  flot-marcelle     None     1919-11-17   1920-11-17                  0  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subscriptions_df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "sh6QhVqFJ8ZH",
        "outputId": "b10ed19e-435e-4ea9-bfdd-045e81cfdf58"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>documented_borrows</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11153</th>\n",
              "      <td>Renewal</td>\n",
              "      <td>1930-03-05</td>\n",
              "      <td>1931-03-05</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Helene Brémond</td>\n",
              "      <td>Brémond, Helene</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 year</td>\n",
              "      <td>365.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Helene Bremond Lending Library C...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/cb...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>bremond-helene</td>\n",
              "      <td>None</td>\n",
              "      <td>1930-03-05</td>\n",
              "      <td>1931-03-05</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11234</th>\n",
              "      <td>Renewal</td>\n",
              "      <td>1930-04-06</td>\n",
              "      <td>1931-04-06</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Renée Antoine-May</td>\n",
              "      <td>Antoine-May, Renée</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1 year</td>\n",
              "      <td>365.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Renée Antoine-May Lending Librar...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/7c...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>antoine-may</td>\n",
              "      <td>None</td>\n",
              "      <td>1930-04-06</td>\n",
              "      <td>1931-04-06</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      event_type  start_date    end_date  \\\n",
              "11153    Renewal  1930-03-05  1931-03-05   \n",
              "11234    Renewal  1930-04-06  1931-04-06   \n",
              "\n",
              "                                             member_uris       member_names  \\\n",
              "11153  https://shakespeareandco.princeton.edu/members...     Helene Brémond   \n",
              "11234  https://shakespeareandco.princeton.edu/members...  Renée Antoine-May   \n",
              "\n",
              "        member_sort_names  subscription_price_paid  subscription_deposit  \\\n",
              "11153     Brémond, Helene                    240.0                   NaN   \n",
              "11234  Antoine-May, Renée                    240.0                   NaN   \n",
              "\n",
              "      subscription_duration  subscription_duration_days  ...  \\\n",
              "11153                1 year                       365.0  ...   \n",
              "11234                1 year                       365.0  ...   \n",
              "\n",
              "                                         source_citation  \\\n",
              "11153  Sylvia Beach, Helene Bremond Lending Library C...   \n",
              "11234  Sylvia Beach, Renée Antoine-May Lending Librar...   \n",
              "\n",
              "                                         source_manifest  \\\n",
              "11153  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "11234  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "\n",
              "                                            source_image  \\\n",
              "11153  https://iiif.princeton.edu/loris/figgy_prod/cb...   \n",
              "11234  https://iiif.princeton.edu/loris/figgy_prod/7c...   \n",
              "\n",
              "                                        first_member_uri second_member_uri  \\\n",
              "11153  https://shakespeareandco.princeton.edu/members...              None   \n",
              "11234  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "            member_id  item_id start_datetime end_datetime documented_borrows  \n",
              "11153  bremond-helene     None     1930-03-05   1931-03-05                 29  \n",
              "11234     antoine-may     None     1930-04-06   1931-04-06                 30  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Set the date for which we want to tally active subscriptions.\n",
        "tally_date = pd.to_datetime(date(1931, 2, 28))\n",
        "\n",
        "# Filter the 'subscriptions_df' DataFrame to include only rows where the subscription is active on 'tally_date'.\n",
        "# A subscription is considered active if its start date is on or before 'tally_date' and its end date is on or after 'tally_date'.\n",
        "# This can help us understand how many subscriptions were active on a specific date.\n",
        "tally_subs = subscriptions_df[(subscriptions_df.start_datetime <= tally_date) & (subscriptions_df.end_datetime >= tally_date)]\n",
        "\n",
        "# Display the first two rows of 'tally_subs' for a quick check.\n",
        "tally_subs.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEGCRYmFKiqG",
        "outputId": "5bcd8a98-2791-4f32-be76-44987d7fa633"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of active subscriptions on 1931-02-28: 31\n",
            "Number of active members on 1931-02-28: 30\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "count    31.000000\n",
              "mean      1.161290\n",
              "std       0.454369\n",
              "min       1.000000\n",
              "25%       1.000000\n",
              "50%       1.000000\n",
              "75%       1.000000\n",
              "max       3.000000\n",
              "Name: subscription_volumes, dtype: float64"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Print the number of active subscriptions on the tally date.\n",
        "print(f\"Number of active subscriptions on {tally_date.strftime('%Y-%m-%d')}: {tally_subs.shape[0]}\")\n",
        "\n",
        "# Print the number of active members on the tally date.\n",
        "print(f\"Number of active members on {tally_date.strftime('%Y-%m-%d')}: {len(tally_subs.member_id.unique())}\")\n",
        "\n",
        "# how many subscription volumes?\n",
        "\n",
        "tally_subs.subscription_volumes.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ya_DoSbcKy4Q",
        "outputId": "bfe978c9-1ba2-4260-f171-818547b9cd1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total subscription volumes for active subscriptions on 1931-02-28: 36.0\n",
            "Number of subscriptions with volume = 1: 27\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "1.0    27\n",
              "2.0     3\n",
              "3.0     1\n",
              "Name: subscription_volumes, dtype: int64"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Print the total subscription volumes for active subscriptions on the tally date.\n",
        "print(f\"Total subscription volumes for active subscriptions on {tally_date.strftime('%Y-%m-%d')}: {tally_subs.subscription_volumes.sum()}\")\n",
        "\n",
        "# how many subscriptions with volume = 1 ?\n",
        "print(f\"Number of subscriptions with volume = 1: {tally_subs[tally_subs.subscription_volumes == 1].shape[0]}\")\n",
        "tally_subs.subscription_volumes.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "h8SUDBewPjCw",
        "outputId": "62752c53-dee0-4531-bfbf-e5e99af9627b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_names</th>\n",
              "      <th>subscription_volumes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11369</th>\n",
              "      <td>Renewal</td>\n",
              "      <td>1930-06-23</td>\n",
              "      <td>1931-06-23</td>\n",
              "      <td>Mr. O'Conor</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11670</th>\n",
              "      <td>Renewal</td>\n",
              "      <td>1930-11-01</td>\n",
              "      <td>1931-11-01</td>\n",
              "      <td>Fernand Colens</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11753</th>\n",
              "      <td>Renewal</td>\n",
              "      <td>1930-12-09</td>\n",
              "      <td>1931-03-09</td>\n",
              "      <td>Dolly Wilde / Miss Dorothy Wilde</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33610</th>\n",
              "      <td>Renewal</td>\n",
              "      <td>1931-02-14</td>\n",
              "      <td>1931-05-14</td>\n",
              "      <td>Tolstoy</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      event_type  start_date    end_date                      member_names  \\\n",
              "11369    Renewal  1930-06-23  1931-06-23                       Mr. O'Conor   \n",
              "11670    Renewal  1930-11-01  1931-11-01                    Fernand Colens   \n",
              "11753    Renewal  1930-12-09  1931-03-09  Dolly Wilde / Miss Dorothy Wilde   \n",
              "33610    Renewal  1931-02-14  1931-05-14                           Tolstoy   \n",
              "\n",
              "       subscription_volumes  \n",
              "11369                   2.0  \n",
              "11670                   2.0  \n",
              "11753                   3.0  \n",
              "33610                   2.0  "
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# which members had subscriptions with volume > 1 ?\n",
        "tally_subs[tally_subs.subscription_volumes > 1][['event_type', 'start_date', 'end_date', 'member_names', 'subscription_volumes']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "-jJLoLozK5Hd",
        "outputId": "68ac0d12-03cd-44c3-ad6b-6add33862af5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>within_subscription</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2652</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1922-12-30</td>\n",
              "      <td>1932-05-02</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Jacques Porel</td>\n",
              "      <td>Porel, Jacques</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Jacques Porel Lending Library Ca...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/7f...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>porel</td>\n",
              "      <td>hardy-trumpet-major</td>\n",
              "      <td>1922-12-30</td>\n",
              "      <td>1932-05-02</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10818</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1929-10-26</td>\n",
              "      <td>1931-11-14</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Anatole Rivoallan</td>\n",
              "      <td>Rivoallan, Anatole</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Rivoallan Lending Library Card, ...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif-cloud.princeton.edu/iiif/2/e0%2F1...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>rivoallan-anatole</td>\n",
              "      <td>deirdre</td>\n",
              "      <td>1929-10-26</td>\n",
              "      <td>1931-11-14</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      event_type  start_date    end_date  \\\n",
              "2652      Borrow  1922-12-30  1932-05-02   \n",
              "10818     Borrow  1929-10-26  1931-11-14   \n",
              "\n",
              "                                             member_uris       member_names  \\\n",
              "2652   https://shakespeareandco.princeton.edu/members...      Jacques Porel   \n",
              "10818  https://shakespeareandco.princeton.edu/members...  Anatole Rivoallan   \n",
              "\n",
              "        member_sort_names  subscription_price_paid  subscription_deposit  \\\n",
              "2652       Porel, Jacques                      NaN                   NaN   \n",
              "10818  Rivoallan, Anatole                      NaN                   NaN   \n",
              "\n",
              "      subscription_duration  subscription_duration_days  ...  \\\n",
              "2652                    NaN                         NaN  ...   \n",
              "10818                   NaN                         NaN  ...   \n",
              "\n",
              "                                         source_citation  \\\n",
              "2652   Sylvia Beach, Jacques Porel Lending Library Ca...   \n",
              "10818  Sylvia Beach, Rivoallan Lending Library Card, ...   \n",
              "\n",
              "                                         source_manifest  \\\n",
              "2652   https://figgy.princeton.edu/concern/scanned_re...   \n",
              "10818  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "\n",
              "                                            source_image  \\\n",
              "2652   https://iiif.princeton.edu/loris/figgy_prod/7f...   \n",
              "10818  https://iiif-cloud.princeton.edu/iiif/2/e0%2F1...   \n",
              "\n",
              "                                        first_member_uri second_member_uri  \\\n",
              "2652   https://shakespeareandco.princeton.edu/members...              None   \n",
              "10818  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "               member_id              item_id start_datetime end_datetime  \\\n",
              "2652               porel  hardy-trumpet-major     1922-12-30   1932-05-02   \n",
              "10818  rivoallan-anatole              deirdre     1929-10-26   1931-11-14   \n",
              "\n",
              "      within_subscription  \n",
              "2652                 True  \n",
              "10818                True  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# how many borrows do we have for that date?\n",
        "tally_borrows = borrow_events_df[(borrow_events_df.start_datetime <= tally_date) & (borrow_events_df.end_datetime >= tally_date)]\n",
        "tally_borrows.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39VnmnfNLMKX",
        "outputId": "e8e2d201-9a6d-4b7b-d617-6e4f839a66f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# how many books out?\n",
        "tally_borrows.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TaudDIgtLRi-",
        "outputId": "891d66e2-7e2b-48b2-83e8-556b8ad2f9d0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# how many members?\n",
        "len(tally_borrows.member_id.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vVL6wqZVN-e",
        "outputId": "37f0b3ca-d104-47b0-bcc2-12ca82edefe4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2.0833333333333335"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "75/36"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8boCObyLgU5"
      },
      "source": [
        "### Percentage of borrowing from early period"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "tHm8LHq4Les_",
        "outputId": "55e4f435-8440-4afe-99cc-b7121dd7397d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>ratio</th>\n",
              "      <th>non_book_subs_vols</th>\n",
              "      <th>estimated_borrow_events</th>\n",
              "      <th>total_estimated_borrow_events</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1919-11-23</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.786014</td>\n",
              "      <td>12.786014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1919-11-30</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>20.0</td>\n",
              "      <td>12.857808</td>\n",
              "      <td>17.857808</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  book_subscription_volumes  borrow_event_count  \\\n",
              "0 1919-11-23                        5.0                   7   \n",
              "1 1919-11-30                        7.0                   5   \n",
              "\n",
              "   card_subscription_volumes  subscription_volumes     ratio  \\\n",
              "0                        6.0                  14.0  1.400000   \n",
              "1                        9.0                  27.0  0.714286   \n",
              "\n",
              "   non_book_subs_vols  estimated_borrow_events  total_estimated_borrow_events  \n",
              "0                 9.0                 5.786014                      12.786014  \n",
              "1                20.0                12.857808                      17.857808  "
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# first logbook gap is January 01 1928\n",
        "\n",
        "first_gap_start = pd.to_datetime(date(1928, 1, 1))\n",
        "\n",
        "pre_first_gap_borrowing = borrowing_weekly[borrowing_weekly.date < first_gap_start]\n",
        "pre_first_gap_borrowing.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5DOT1N0MCTI",
        "outputId": "40008071-90dc-410e-c51c-b16d4b47acde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total estimated borrow events before the first gap: 46024.66714941317\n",
            "Total known borrow events before the first gap: 4159\n",
            "Ratio of known borrow events to estimated borrow events: 0.09036458615764326\n",
            "Percentage of estimated borrow events that were actually recorded: 9.036458615764326%\n"
          ]
        }
      ],
      "source": [
        "# Calculate the total estimated borrow events before the first gap.\n",
        "# This can give us an idea of how many borrow events we would expect based on the subscriptions.\n",
        "pregap_est_total = pre_first_gap_borrowing.total_estimated_borrow_events.sum()\n",
        "print(f\"Total estimated borrow events before the first gap: {pregap_est_total}\")\n",
        "\n",
        "# Calculate the total known borrow events before the first gap.\n",
        "# This can help us understand how many borrow events were actually recorded.\n",
        "pregap_known_total = pre_first_gap_borrowing.borrow_event_count.sum()\n",
        "print(f\"Total known borrow events before the first gap: {pregap_known_total}\")\n",
        "\n",
        "# Calculate the ratio of known borrow events to estimated borrow events.\n",
        "# This can help us understand what proportion of the estimated borrow events were actually recorded.\n",
        "ratio = pregap_known_total / pregap_est_total\n",
        "print(f\"Ratio of known borrow events to estimated borrow events: {ratio}\")\n",
        "\n",
        "# Convert the ratio to a percentage for easier interpretation.\n",
        "percentage = ratio * 100\n",
        "print(f\"Percentage of estimated borrow events that were actually recorded: {percentage}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnUMv5TSMg9b"
      },
      "source": [
        "### Percentage of borrowing in the 1930s between gaps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "HovTACz4MqBG",
        "outputId": "61b81300-5d44-45ce-b869-7ef82c1b2c33"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>book_subscription_volumes</th>\n",
              "      <th>borrow_event_count</th>\n",
              "      <th>card_subscription_volumes</th>\n",
              "      <th>subscription_volumes</th>\n",
              "      <th>ratio</th>\n",
              "      <th>non_book_subs_vols</th>\n",
              "      <th>estimated_borrow_events</th>\n",
              "      <th>total_estimated_borrow_events</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>1932-10-02</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.500233</td>\n",
              "      <td>12.500233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>1932-10-09</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>10.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.286247</td>\n",
              "      <td>19.286247</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          date  book_subscription_volumes  borrow_event_count  \\\n",
              "671 1932-10-02                       10.0                   8   \n",
              "672 1932-10-09                       10.0                   9   \n",
              "\n",
              "     card_subscription_volumes  subscription_volumes  ratio  \\\n",
              "671                       10.0                  17.0    0.8   \n",
              "672                       10.0                  26.0    0.9   \n",
              "\n",
              "     non_book_subs_vols  estimated_borrow_events  \\\n",
              "671                 7.0                 4.500233   \n",
              "672                16.0                10.286247   \n",
              "\n",
              "     total_estimated_borrow_events  \n",
              "671                      12.500233  \n",
              "672                      19.286247  "
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Define the start and end dates for the period in the 1930s between gaps.\n",
        "# This period is from September 25, 1932 to January 1, 1937.\n",
        "# The 'pd.to_datetime' function is used to convert the dates to datetime objects.\n",
        "mid_gap_start = pd.to_datetime(date(1932, 9, 25))\n",
        "mid_gap_end =  pd.to_datetime(date(1937, 1, 1))\n",
        "\n",
        "# Filter the 'borrowing_weekly' DataFrame to include only rows where the date is within the defined period.\n",
        "# This can help us analyze borrowing activity during this specific time period.\n",
        "mid_gap_borrowing = borrowing_weekly[(borrowing_weekly.date > mid_gap_start) & (borrowing_weekly.date < mid_gap_end)]\n",
        "\n",
        "# Display the 'mid_gap_borrowing' DataFrame for a quick check.\n",
        "mid_gap_borrowing.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZ01SVZkNGFQ",
        "outputId": "79b929dc-d611-4ae1-f1c2-3deec72a5cef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Estimated total borrow events during the mid-gap period: 10111.2744170707\n"
          ]
        }
      ],
      "source": [
        "# what is the estimated borrowing vs actual?\n",
        "\n",
        "mid_gap_borrowing_est_total = mid_gap_borrowing.total_estimated_borrow_events.sum()\n",
        "\n",
        "print(f\"Estimated total borrow events during the mid-gap period: {mid_gap_borrowing_est_total}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "ppvcHzTbPXjB",
        "outputId": "c45c89a1-32de-49a0-b330-7963679f1ffb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>item_year</th>\n",
              "      <th>item_notes</th>\n",
              "      <th>source_type</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1922</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Mr. Rhys</td>\n",
              "      <td>Rhys, Mr.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1902.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lending Library Card</td>\n",
              "      <td>Sylvia Beach, Rhys Lending Library Card, Box 4...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/67...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>rhys</td>\n",
              "      <td>conrad-typhoon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1923</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Eyre de Lanux / Mrs. Pierre de Lanux</td>\n",
              "      <td>de Lanux, Eyre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1919.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lending Library Card</td>\n",
              "      <td>Sylvia Beach, Eyre de Lanux Lending Library Ca...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/c5...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>lanux-eyre-de</td>\n",
              "      <td>woolf-night-day</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  event_type start_date    end_date  \\\n",
              "2     Borrow       1922  1922-08-23   \n",
              "8     Borrow       1923  1923-11-07   \n",
              "\n",
              "                                         member_uris  \\\n",
              "2  https://shakespeareandco.princeton.edu/members...   \n",
              "8  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "                           member_names member_sort_names  \\\n",
              "2                              Mr. Rhys         Rhys, Mr.   \n",
              "8  Eyre de Lanux / Mrs. Pierre de Lanux    de Lanux, Eyre   \n",
              "\n",
              "   subscription_price_paid  subscription_deposit subscription_duration  \\\n",
              "2                      NaN                   NaN                   NaN   \n",
              "8                      NaN                   NaN                   NaN   \n",
              "\n",
              "   subscription_duration_days  ...  item_year item_notes  \\\n",
              "2                         NaN  ...     1902.0        NaN   \n",
              "8                         NaN  ...     1919.0        NaN   \n",
              "\n",
              "            source_type                                    source_citation  \\\n",
              "2  Lending Library Card  Sylvia Beach, Rhys Lending Library Card, Box 4...   \n",
              "8  Lending Library Card  Sylvia Beach, Eyre de Lanux Lending Library Ca...   \n",
              "\n",
              "                                     source_manifest  \\\n",
              "2  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "8  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "\n",
              "                                        source_image  \\\n",
              "2  https://iiif.princeton.edu/loris/figgy_prod/67...   \n",
              "8  https://iiif.princeton.edu/loris/figgy_prod/c5...   \n",
              "\n",
              "                                    first_member_uri second_member_uri  \\\n",
              "2  https://shakespeareandco.princeton.edu/members...              None   \n",
              "8  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "       member_id          item_id  \n",
              "2           rhys   conrad-typhoon  \n",
              "8  lanux-eyre-de  woolf-night-day  \n",
              "\n",
              "[2 rows x 32 columns]"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# borrows with any date, to improve our reporting by period\n",
        "\n",
        "all_borrows = events_df[events_df.event_type == 'Borrow']\n",
        "all_borrows.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "PzUHpat8PtDe",
        "outputId": "8bf122dc-df68-4ede-d26d-adaaf91c8d2d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1922</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>1922-08-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1923</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>1923-11-07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  start_date    end_date        date\n",
              "2       1922  1922-08-23  1922-08-23\n",
              "8       1923  1923-11-07  1923-11-07"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The best date is defined as the longest string between the start date and end date.\n",
        "# If one of the dates is missing (NaN), the other date is returned.\n",
        "# If both dates are the same length, the end date is returned.\n",
        "def get_best_date(row):\n",
        "  # Calculate the length of the start date string, or 0 if it's NaN.\n",
        "  start_date_len = len(row.start_date) if pd.notna(row.start_date) else 0\n",
        "  # Calculate the length of the end date string, or 0 if it's NaN.\n",
        "  end_date_len = len(row.end_date) if pd.notna(row.end_date) else 0\n",
        "\n",
        "  # Return the start date if it's longer, otherwise return the end date.\n",
        "  if start_date_len > end_date_len:\n",
        "    return row.start_date\n",
        "  else:\n",
        "    return row.end_date\n",
        "\n",
        "# Apply the 'get_best_date' function to each row in 'all_borrows'.\n",
        "all_borrows['date'] = all_borrows.apply(get_best_date, axis=1)\n",
        "\n",
        "# Display the start date, end date, and best date for the first few rows for a quick check.\n",
        "all_borrows[['start_date', 'end_date', 'date']].head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UDWI7LlQi0o",
        "outputId": "69da45bd-06fe-4580-93d2-1e4174fae62c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of borrow events with missing dates: 6\n"
          ]
        }
      ],
      "source": [
        "# how many are still null?\n",
        "print(f\"Number of borrow events with missing dates: {all_borrows[all_borrows.date.isna()].shape[0]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "OyzwGzUPQrEe",
        "outputId": "6b5e238a-ee3b-4068-86cd-6bccf7828a04"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>date</th>\n",
              "      <th>dt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1922</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>1922-08-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1923</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>1923-11-07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  start_date    end_date        date         dt\n",
              "2       1922  1922-08-23  1922-08-23 1922-08-23\n",
              "8       1923  1923-11-07  1923-11-07 1923-11-07"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# convert to datetime for comparison/filtering\n",
        "all_borrows['dt'] = pd.to_datetime(all_borrows.date, errors='coerce')\n",
        "all_borrows[['start_date', 'end_date', 'date', 'dt']].head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "VaD84s3hNRmp"
      },
      "outputs": [],
      "source": [
        "# This function can be used to compare the estimated borrowing events with the actual borrowing events. This can help us understand how accurate our estimates are and how much data might be missing.\n",
        "def borrowing_estimate_by_date(borrowing_weekly, start_date=None, end_date=None):\n",
        "  # Copy the 'borrowing_weekly' and 'all_borrows' DataFrames to avoid modifying the original data.\n",
        "  borrowing_chunk = borrowing_weekly.copy()\n",
        "  period_borrows = all_borrows.copy()\n",
        "  \n",
        "  # If a start date is provided, filter the data to include only rows where the date is after the start date.\n",
        "  if start_date:\n",
        "    borrowing_chunk = borrowing_chunk[borrowing_chunk.date > start_date]\n",
        "    period_borrows = period_borrows[period_borrows.dt > start_date]\n",
        "  # If an end date is provided, filter the data to include only rows where the date is before the end date.\n",
        "  if end_date:\n",
        "    borrowing_chunk = borrowing_chunk[borrowing_chunk.date < end_date]\n",
        "    period_borrows = period_borrows[period_borrows.dt < end_date]\n",
        "\n",
        "  # Calculate the total estimated borrow events for the period.\n",
        "  est_total = round(borrowing_chunk.total_estimated_borrow_events.sum())\n",
        "  # Calculate the total known borrow events for the period.\n",
        "  known_total = borrowing_chunk.borrow_event_count.sum()  \n",
        "  # Calculate the total number of borrow events with any date for the period.\n",
        "  total_any_borrows = period_borrows.shape[0]\n",
        "\n",
        "  # Calculate the percentage of estimated borrow events that are known.\n",
        "  percent_total = total_any_borrows / est_total * 100\n",
        "\n",
        "  # Print the results.\n",
        "  print(f\"\"\"Estimated borrowing between {start_date or ''} — {end_date or ''}:\n",
        "  estimated total borrowing events: {est_total}\n",
        "  known borrowing events (with full dates): {known_total}\n",
        "  known borrowing events (with any dates): {total_any_borrows}\n",
        "  surviving percent: {percent_total:.2f}\n",
        "  \"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZb9EpisOJdT",
        "outputId": "0a6c75b0-9bf8-4c56-9344-f412a86c48a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Estimated borrowing between  — 1928-01-01 00:00:00:\n",
            "  estimated total borrowing events: 46025\n",
            "  known borrowing events (with full dates): 4159\n",
            "  known borrowing events (with any dates): 4459\n",
            "  surviving percent: 9.69\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "# Before the first gap\n",
        "borrowing_estimate_by_date(borrowing_weekly, end_date=first_gap_start)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDPMFVOdOa8J",
        "outputId": "2b1dc7fc-9812-43ec-d0ab-367c8b23a671"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Estimated borrowing between 1932-09-25 00:00:00 — 1937-01-01 00:00:00:\n",
            "  estimated total borrowing events: 10111\n",
            "  known borrowing events (with full dates): 4810\n",
            "  known borrowing events (with any dates): 5003\n",
            "  surviving percent: 49.48\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "# between logbook gaps in the 1930s\n",
        "\n",
        "mid_gap_start = pd.to_datetime(date(1932, 9, 25))\n",
        "mid_gap_end =  pd.to_datetime(date(1937, 1, 1))\n",
        "\n",
        "borrowing_estimate_by_date(borrowing_weekly, mid_gap_start, mid_gap_end)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x987nRObOveT",
        "outputId": "c2695dc7-fc54-491b-db8f-21d0f52544d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Estimated borrowing between 1939-09-12 00:00:00 — :\n",
            "  estimated total borrowing events: 6503\n",
            "  known borrowing events (with full dates): 2790\n",
            "  known borrowing events (with any dates): 3436\n",
            "  surviving percent: 52.84\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "# after the last logbook gap\n",
        "# \tAugust 29 1939 to September 12 1939 (14 days)\n",
        "\n",
        "last_gap_end = pd.to_datetime(date(1939, 9, 12))\n",
        "\n",
        "borrowing_estimate_by_date(borrowing_weekly, last_gap_end)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "do_TgwHqI67d"
      },
      "source": [
        "### All borrows, official borrows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrVUSN1OHjot",
        "outputId": "a948f7d6-60ff-4c0a-ce07-487af18d1c0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['1922', '1923', '1924', '1926', '1929', '1931', '1934', '1936',\n",
              "       '1937', '1938', '1940', '1942', '1944', '1946', '1948', '1952',\n",
              "       '1955', '1956', '1960', '1920', '1921', '1927', '1928', '1932',\n",
              "       '1935', None, '1939', '1943', '1945', '1947', '1954', '1961', '',\n",
              "       '1919', '1925', '1930', '1933', '1941', '1949', '1950', '1951',\n",
              "       '1953', '1957', '1958', '1962'], dtype=object)"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Extract the year from the start date of each borrow event in 'all_borrows'.\n",
        "# If the start date is NaN, None is returned.\n",
        "# The result is stored in a new column 'year'.\n",
        "all_borrows['year'] = all_borrows.start_date.apply(lambda x: x.split('-')[0] if pd.notna(x) else None)\n",
        "\n",
        "# Display the unique years in the 'year' column for a quick check.\n",
        "all_borrows.year.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "bS0R1CEgHl2j",
        "outputId": "87747f5d-528a-4caa-8342-2de209821dae"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>event_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>member_uris</th>\n",
              "      <th>member_names</th>\n",
              "      <th>member_sort_names</th>\n",
              "      <th>subscription_price_paid</th>\n",
              "      <th>subscription_deposit</th>\n",
              "      <th>subscription_duration</th>\n",
              "      <th>subscription_duration_days</th>\n",
              "      <th>...</th>\n",
              "      <th>source_citation</th>\n",
              "      <th>source_manifest</th>\n",
              "      <th>source_image</th>\n",
              "      <th>first_member_uri</th>\n",
              "      <th>second_member_uri</th>\n",
              "      <th>member_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>date</th>\n",
              "      <th>dt</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1922</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Mr. Rhys</td>\n",
              "      <td>Rhys, Mr.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Rhys Lending Library Card, Box 4...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/67...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>rhys</td>\n",
              "      <td>conrad-typhoon</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>1922-08-23</td>\n",
              "      <td>1922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Borrow</td>\n",
              "      <td>1923</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>Eyre de Lanux / Mrs. Pierre de Lanux</td>\n",
              "      <td>de Lanux, Eyre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>Sylvia Beach, Eyre de Lanux Lending Library Ca...</td>\n",
              "      <td>https://figgy.princeton.edu/concern/scanned_re...</td>\n",
              "      <td>https://iiif.princeton.edu/loris/figgy_prod/c5...</td>\n",
              "      <td>https://shakespeareandco.princeton.edu/members...</td>\n",
              "      <td>None</td>\n",
              "      <td>lanux-eyre-de</td>\n",
              "      <td>woolf-night-day</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>1923-11-07</td>\n",
              "      <td>1923</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 35 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  event_type start_date    end_date  \\\n",
              "2     Borrow       1922  1922-08-23   \n",
              "8     Borrow       1923  1923-11-07   \n",
              "\n",
              "                                         member_uris  \\\n",
              "2  https://shakespeareandco.princeton.edu/members...   \n",
              "8  https://shakespeareandco.princeton.edu/members...   \n",
              "\n",
              "                           member_names member_sort_names  \\\n",
              "2                              Mr. Rhys         Rhys, Mr.   \n",
              "8  Eyre de Lanux / Mrs. Pierre de Lanux    de Lanux, Eyre   \n",
              "\n",
              "   subscription_price_paid  subscription_deposit subscription_duration  \\\n",
              "2                      NaN                   NaN                   NaN   \n",
              "8                      NaN                   NaN                   NaN   \n",
              "\n",
              "   subscription_duration_days  ...  \\\n",
              "2                         NaN  ...   \n",
              "8                         NaN  ...   \n",
              "\n",
              "                                     source_citation  \\\n",
              "2  Sylvia Beach, Rhys Lending Library Card, Box 4...   \n",
              "8  Sylvia Beach, Eyre de Lanux Lending Library Ca...   \n",
              "\n",
              "                                     source_manifest  \\\n",
              "2  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "8  https://figgy.princeton.edu/concern/scanned_re...   \n",
              "\n",
              "                                        source_image  \\\n",
              "2  https://iiif.princeton.edu/loris/figgy_prod/67...   \n",
              "8  https://iiif.princeton.edu/loris/figgy_prod/c5...   \n",
              "\n",
              "                                    first_member_uri second_member_uri  \\\n",
              "2  https://shakespeareandco.princeton.edu/members...              None   \n",
              "8  https://shakespeareandco.princeton.edu/members...              None   \n",
              "\n",
              "       member_id          item_id        date         dt  year  \n",
              "2           rhys   conrad-typhoon  1922-08-23 1922-08-23  1922  \n",
              "8  lanux-eyre-de  woolf-night-day  1923-11-07 1923-11-07  1923  \n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "official_borrows = all_borrows[all_borrows.year < \"1942\"]\n",
        "official_borrows.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZ0KEi_oH0Nv"
      },
      "source": [
        "### Report on excluded events"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMcblbE4HnTX",
        "outputId": "69bc5e85-c40f-43ab-cb0f-b5a39f5114f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of borrow events: 21059\n",
            "Total number of official borrow events: 20597\n",
            "Difference between total and official borrow events: 462\n",
            "Ratio of official borrow events to total borrow events: 0.9780616363550027\n",
            "Total number of book events: 22484\n",
            "Ratio of official borrow events to total book events: 0.9160736523750223\n",
            "Number of official borrow events used to generate estimate: 17292\n",
            "Estimated total borrow events based on subscriptions: 80622.49254804966\n",
            "Ratio of official borrow events to estimated total: 0.2554746119728875\n"
          ]
        }
      ],
      "source": [
        "# Calculate the total number of borrow events in 'events_df'.\n",
        "total_borrows = events_df[events_df.event_type == 'Borrow'].shape[0]\n",
        "print(f\"Total number of borrow events: {total_borrows}\")\n",
        "\n",
        "# Calculate the total number of official borrow events.\n",
        "total_official_borrows = official_borrows.shape[0]\n",
        "print(f\"Total number of official borrow events: {total_official_borrows}\")\n",
        "\n",
        "# Calculate the difference between the total number of borrow events and the total number of official borrow events. This can help us understand how many borrow events are not officially recorded.\n",
        "difference = total_borrows - total_official_borrows\n",
        "print(f\"Difference between total and official borrow events: {difference}\")\n",
        "\n",
        "# Calculate the ratio of official borrow events to total borrow events. This can help us understand what proportion of the borrow events are officially recorded.\n",
        "ratio = total_official_borrows / total_borrows\n",
        "print(f\"Ratio of official borrow events to total borrow events: {ratio}\")\n",
        "\n",
        "# what about other book activity?\n",
        "book_events = events_df[events_df.item_uri.notna()]\n",
        "total_book_events = book_events.shape[0]\n",
        "print(f\"Total number of book events: {total_book_events}\")\n",
        "\n",
        "# Calculate the ratio of official borrow events to total book events. This can help us understand what proportion of the book events are official borrows.\n",
        "ratio = total_official_borrows / total_book_events\n",
        "print(f\"Ratio of official borrow events to total book events: {ratio}\")\n",
        "\n",
        "# Calculate the total official borrows minus the total borrows used to estimate.\n",
        "# This can help us understand how many official borrows were used to generate our estimate.\n",
        "total_borrows_used_to_estimate = total_official_borrows - total_borrows_used_to_estimate\n",
        "print(f\"Number of official borrow events used to generate estimate: {total_borrows_used_to_estimate}\")\n",
        "\n",
        "# Calculate estimated total based on subscriptions\n",
        "est_total = est_total_missing_borrows + total_borrows_used_to_estimate\n",
        "print(f\"Estimated total borrow events based on subscriptions: {est_total}\")\n",
        "\n",
        "# Calculate the ratio of official borrow events to the estimated total.\n",
        "# This can help us understand what proportion of the estimated borrow events are officially recorded.\n",
        "ratio = total_official_borrows / est_total\n",
        "print(f\"Ratio of official borrow events to estimated total: {ratio}\")\n"
      ]
    }
  ],
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